papers.bib

@inproceedings{kelber2026retrieval,
  author    = {Florian Kelber and Matthias Jobst and Yuni Susanti and Michael F{\"a}rber},
  title     = {Do We Need Bigger Models for Science? Task-Aware Retrieval with Small Language Models},
  booktitle   = {LREC},
  year      = {2026},
  url       = {https://arxiv.org/abs/2604.01965},
  abstract  = {Scientific knowledge discovery increasingly relies on large language models, yet many existing scholarly assistants depend on proprietary systems with tens or hundreds of billions of parameters. We investigate whether carefully designed retrieval pipelines can compensate for reduced model scale. We propose a lightweight retrieval-augmented framework with task-aware routing that selects specialized retrieval strategies based on the query, integrates full-text papers and structured metadata, and uses compact instruction-tuned models for citation-grounded responses. Evaluations across scholarly QA, multi-document reasoning, biomedical QA under domain shift, and text compression show that retrieval and model scale are complementary: retrieval design can partially offset smaller models, but model capacity remains crucial for complex reasoning.},
  tldr      = {We show that task-aware retrieval pipelines combined with small instruction-tuned models can achieve strong performance on scholarly tasks, partially compensating for reduced model size. However, model scale remains important for complex reasoning, highlighting a complementary relationship between retrieval design and model capacity.},
  plain     = {Do we really need huge AI models for science? Not always. With smart retrieval of relevant papers, smaller models can already perform well—but very complex reasoning still benefits from larger models.},
  keywords  = {retrieval-augmented generation, scholarly QA, small language models, task-aware retrieval},
  pdf       = {publications/Task-Aware_Retrieval_NSLP-LREC2026.pdf}
}

@inproceedings{besrour2026unarxive,
  author    = {Ines Besrour and Michael F{\"a}rber},
  title     = {unarXive 2024: A Large-Scale Scientific Corpus for Citation-Aware Retrieval and Generation},
  booktitle = {LREC},
  year      = {2026},
  abstract  = {Full-text collections of scientific papers are essential for NLP research and the training of language models. However, existing resources remain incomplete: they often lag behind the fast-paced growth of scientific publishing, lack comprehensive citation networks, and discard essential structural elements. In this work, we introduce unarXive 2024, a large-scale, richly structured corpus containing every arXiv submission from January 1991 to December 2024 – over 2.28 million documents across physics, mathematics, computer science, and other fields. Our release enhances each paper with detailed metadata, reconstructs a substantially more complete citation network than existing datasets, and preserves fine-grained structural information, including section boundaries, mathematical notation, and non-textual elements. Beyond the corpus itself, we provide dense and sparse indexes optimized for retrieval-augmented generation (RAG) over the full arXiv archive. All resources, including code and data, are publicly available: https://github.com/faerber-lab/unarXive-2024},
  url       = {https://github.com/faerber-lab/unarXive-2024},
  pdf       = {publications/unarXive_LREC2026.pdf},
  tldr      = {We introduce unarXive 2024, a 2.28M-paper corpus with improved citation resolution (39.5M links) and structured full text, enabling large-scale citation-aware retrieval and RAG.},
  plain     = {We collected millions of scientific papers and organized them so computers can better understand what they say and how they reference each other.}
}

@inproceedings{schopf2026idea,
  author    = {Tim Schopf and Michael F{\"a}rber},
  title     = {Is this Idea Novel? An Automated Benchmark for Judgment of Research Ideas},
  booktitle = {LREC},
  year      = {2026},
  abstract  = {Judging the novelty of research ideas is crucial for advancing science, enabling the identification of unexplored directions, and ensuring contributions meaningfully extend existing knowledge rather than reiterate minor variations. However, given the exponential growth of scientific literature, manually judging the novelty of research ideas through literature reviews is labor-intensive, subjective, and infeasible at scale. Therefore, recent efforts have proposed automated approaches for research idea novelty judgment. Yet, evaluation of these approaches remains largely inconsistent and is typically based on non-standardized human evaluations, hindering large-scale, comparable evaluations. To address this, we introduce RINoBench, the first comprehensive benchmark for large-scale evaluation of research idea novelty judgments. It comprises 1,381 research ideas derived from and judged by human experts as well as nine automated evaluation metrics designed to assess both rubric-based novelty scores and textual justifications of novelty judgments. Using this benchmark, we evaluate several state-of-the-art large language models (LLMs) on their ability to judge the novelty of research ideas. Our findings reveal that while LLM-generated reasoning closely mirrors human rationales, this alignment does not reliably translate into accurate novelty judgments, which diverge significantly from human gold standard judgments—even among leading reasoning-capable models.},
  url       = {https://github.com/TimSchopf/RINoBench},
  pdf       = {publications/IdeaNovel_LREC2026.pdf},
  tldr      = {We introduce RINoBench, a large-scale benchmark for evaluating how well language models judge the novelty of research ideas, showing that current models produce plausible reasoning but fail to make accurate novelty judgments.},
  plain     = {We built a dataset to test whether AI can judge if a research idea is new. Current models can explain their reasoning well, but they often still get the final decision wrong.}
}

@inproceedings{yuan2026codae,
  author    = {Shuzhou Yuan and William LaCroix and Hardik Ghoshal and Ercong Nie and Michael F{\"a}rber},
  title     = {CoDAE: Adapting Large Language Models for Education via Chain-of-Thought Data Augmentation},
  booktitle = {LREC},
  year      = {2026},
  url       = {https://arxiv.org/abs/2508.08386},
  doi       = {10.48550/arXiv.2508.08386},
  abstract  = {Large Language Models (LLMs) are increasingly used as AI tutors, but often fail to provide pedagogically appropriate guidance. They may reveal answers too quickly, struggle to adapt to student uncertainty, and remain vulnerable to manipulative prompts. We introduce CoDAE, a framework that adapts LLMs for educational use via Chain-of-Thought (CoT) data augmentation. Using real-world student–tutor dialogues, we enrich training data with step-by-step reasoning and targeted dialogue cases addressing over-compliance, low adaptivity, and threat vulnerability. Fine-tuning multiple open-source LLMs on these datasets leads to improved pedagogical behavior, stronger reasoning support, and increased robustness against premature answer disclosure.},
  tldr      = {We propose CoDAE, a CoT-based data augmentation framework to adapt LLMs for educational settings. Fine-tuned models provide more pedagogically aligned guidance, better reasoning support, and improved robustness against over-compliance and manipulation.},
  plain     = {AI tutors often give answers too quickly or can be manipulated. We improve them by training on better example dialogues, helping them guide students step by step instead of just giving solutions.},
  keywords  = {educational NLP, chain-of-thought, data augmentation, LLM fine-tuning},
  pdf       = {publications/CoDAE_LREC2026.pdf}
}

@inproceedings{thellmann2026eu20,
  author    = {Klaudia Thellmann and Bernhard Stadler and Michael F{\"a}rber},
  title     = {Diagnosing Translated Benchmarks: An Automated Quality Assurance Study of the EU20 Benchmark Suite},
  booktitle   = {LREC},
  year      = {2026},
  url       = {https://arxiv.org/abs/2604.01957},
  keywords  = {machine translation evaluation, benchmark quality, multilingual NLP},
  abstract  = {Machine-translated benchmark datasets reduce costs and offer scale, but noise, loss of structure, and uneven quality weaken confidence. What matters is not merely whether we can translate, but also whether we can measure and verify translation reliability at scale. We study translation quality in the EU20 benchmark suite, which comprises five established benchmarks translated into 20 languages, via a three-step automated quality assurance approach: (i) a structural corpus audit with targeted fixes; (ii) quality profiling using a neural metric (COMET, reference-free and reference-based) with translation service comparisons (DeepL / ChatGPT / Google); and (iii) an LLM-based span-level translation error landscape. Trends are consistent: datasets with lower COMET scores exhibit a higher share of accuracy/mistranslation errors at span level (notably HellaSwag; ARC is comparatively clean). Reference-based COMET on MMLU against human-edited samples points in the same direction. We release cleaned/corrected versions of the EU20 datasets, and code for reproducibility. In sum, automated quality assurance offers practical, scalable indicators that help prioritize review -- complementing, not replacing, human gold standards.},
  tldr      = {We analyze the multilingual EU20 benchmark suite with automated quality assurance methods combining structural checks, COMET-based scoring, and LLM-based error analysis. The results show that automated metrics can reliably surface problematic translated datasets and help prioritize manual review at scale.},
  plain     = {Many AI benchmarks are translated automatically into many languages, but some translations contain serious errors. We show how automatic checks can find these weak spots efficiently and help improve benchmark quality before researchers rely on them.},
  pdf       = {publications/EU20_Benchmarks_LREC2026.pdf}
}

@inproceedings{kummer2026prompt,
  author    = {Cornelius Kummer and Lena Jurkschat and Michael F{\"a}rber and Sahar Vahdati},
  title     = {Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference},
  booktitle = {ECIR},
  year      = {2026},
  location  = {Delft, The Netherlands},
  url       = {https://doi.org/10.1007/978-3-032-21289-4_17},
  pdf      = {publications/PromptCompression_ECIR2026.pdf},
  abstract = {With the widespread adoption of language models in information retrieval, especially in retrieval-augmented generation (RAG) systems, inference latency has become a key bottleneck, as retrieved passages substantially increase prompt length and computational cost. Prompt compression reduces input size while aiming to preserve downstream task performance, but its practical value depends on whether the added preprocessing time is compensated by faster decoding. We present the first systematic large-scale study of this trade-off, based on thousands of runs and 30,000 queries across several open-source LLMs and three GPU classes. Our evaluation disentangles compression overhead from decoding latency while also measuring output quality and memory usage. LLMLingua yields up to 18\% end-to-end speedups when prompt length, compression ratio, and hardware capacity are well aligned, while response quality remains statistically unchanged across summarization, code generation, and question answering tasks. Outside this operating regime, however, compression overhead dominates and offsets the gains. We further show that effective compression can reduce memory usage enough to move workloads from data-center GPUs to commodity graphics cards, with only a 0.3\,s increase in latency. Finally, we release an open-source profiler that predicts the latency break-even point for each model-hardware setup and provides practical guidance on when prompt compression is beneficial in real-world deployments.},
  keywords = {prompt compression, large language models, retrieval-augmented generation, inference latency},
  tldr = {We present the first large-scale empirical study of prompt compression under realistic inference conditions, showing that LLMLingua yields up to 18\% end-to-end latency reduction without statistically significant quality degradation when prompt length, compression ratio, and hardware are properly aligned, while misaligned settings let compression overhead dominate.},
  plain = {Prompt compression can make language models faster and cheaper, but only in the right setting. We show when it helps in practice and when the compression step takes longer than the time it saves.}
}

@article{doi:10.1021/acscentsci.5c02031,
  author   = {Ahlbrecht, Jesse and Lutz, Marius D. R. and Jost, Vera and F{\"a}rber, Michael and Br{\"a}se, Stefan and Wuitschik, Georg},
  title    = {Which Reaction Conditions Work on Drug-Like Molecules? Lessons from 66,000 High-Throughput Experiments},
  journal  = {ACS Central Science},
  volume   = {12},
  number   = {2},
  pages    = {222--232},
  year     = {2026},
  doi      = {10.1021/acscentsci.5c02031},
  URL      = {https://doi.org/10.1021/acscentsci.5c02031},
  abstract = {High-throughput experimentation (HTE) accelerates chemical discovery by shortening the lead times for molecule synthesis. The choice of initial reaction conditions directly influences the outcome and length of any reaction optimization. But human involvement in plate design and data analysis remains a significant cost factor and is accompanied by biases. Therefore, making the most out of past reaction outcomes is crucial. While advances in machine learning allow us to generate promising reaction conditions, this approach is often not suitable because not enough relevant reaction data are available or it is of insufficient quality. Herein we introduce a robust statistical method using z-scores to analyze 66,000 internal HTE reactions on complex molecules. Additionally, we publish the underlying data as well as a tool to analyze and draw actionable conclusions from this data set. We exemplify the power of this method for the widely employed Buchwald--Hartwig and Suzuki--Miyaura cross-coupling reactions. The results reveal optimal conditions that differ significantly from literature-based guidelines. These data-driven insights provide high-quality starting points for optimization campaigns, improving their overall efficiency.},
  keywords = {high-throughput experimentation, reaction conditions, reaction optimization, z-scores, statistical analysis, Buchwald--Hartwig coupling, Suzuki--Miyaura coupling, drug-like molecules, chemical synthesis},
  tldr     = {We analyze 66,000 high-throughput reactions on complex, drug-like molecules using a robust z-score-based statistical method, and release both the dataset and an analysis tool, revealing data-driven optimal conditions for Buchwald--Hartwig and Suzuki--Miyaura couplings that differ markedly from literature guidelines.},
  plain    = {We study outcomes from 66,000 automated chemistry experiments and use a simple, robust statistic (z-scores) to identify which starting reaction settings work best. The released dataset and tool show that the best conditions for common cross-coupling reactions can differ a lot from what textbooks and papers typically recommend, giving better starting points for faster optimization.}
}

@article{DBLP:journals/corr/abs-2601-08668,
  author       = {Kyuri Im and Shuzhou Yuan and Michael Farber},
  title        = {Analyzing Bias in False Refusal Behavior of Large Language Models for Hate Speech Detoxification},
  journal      = {CoRR},
  volume       = {abs/2601.08668},
  year         = {2026},
  url          = {https://doi.org/10.48550/arXiv.2601.08668},
  eprinttype   = {arXiv},
  abstract     = {We study false refusal behavior of large language models in hate speech detoxification. Across nine models and English/multilingual datasets, refusals increase with semantic toxicity and for targets such as nationality, religion, and political ideology, with additional language-dependent biases. We propose a simple cross-translation strategy (English→Chinese→English) that significantly reduces false refusals while preserving content.}
}

@article{DBLP:journals/corr/abs-2602-00279,
  author       = {Philip M{\"{u}}ller and Nicholas Popovic and Michael F{\"{a}}rber and Peter Steinbach},
  title        = {Benchmarking Uncertainty Calibration in Large Language Model Long-Form Question Answering},
  journal      = {CoRR},
  volume       = {abs/2602.00279},
  year         = {2026},
  url          = {https://doi.org/10.48550/arXiv.2602.00279},
  eprinttype   = {arXiv},
  eprint       = {2602.00279},
  abstract     = {Large language models (LLMs) are increasingly used for question answering in the natural sciences and beyond, making reliable uncertainty quantification (UQ) essential for trustworthy deployment. However, existing UQ approaches remain only weakly validated in scientific QA, where factual retrieval and reasoning are both critical. We introduce the first large-scale benchmark for evaluating UQ metrics in reasoning-intensive QA and provide an extensible open-source framework for reproducible calibration assessment. Our study covers up to 20 LLMs, including base, instruction-tuned, and reasoning-oriented variants, and evaluates them on seven scientific QA datasets spanning multiple-choice and arithmetic tasks, formulated through prompting as open QA. In total, we analyze 685,000 long-form responses across varying levels of reasoning complexity representative of domain-specific settings. At the token level, we find that instruction tuning strongly polarizes probability mass, reducing the reliability of token-level confidences as uncertainty estimates. Models additionally fine-tuned for reasoning exhibit the same pattern, although the reasoning process can partially mitigate this effect depending on the provider. At the sequence level, verbalized confidence methods are systematically biased and only weakly correlated with correctness, whereas answer frequency across samples provides the most reliable calibration. We further show that relying exclusively on expected calibration error (ECE) can give a misleading picture of UQ performance on benchmark datasets. Overall, our findings reveal substantial limitations of current UQ methods for LLMs and of standard benchmarking practices in scientific QA.}
}

@article{DBLP:journals/corr/abs-2602-12828,
  author       = {Zhan Qu and
                  Michael F{\"{a}}rber},
  title        = {{GRAIL:} Geometry-Aware Retrieval-Augmented Inference with LLMs over Hyperbolic Representations of Patient Trajectories},
  journal      = {CoRR},
  volume       = {abs/2602.12828},
  year         = {2026},
  url          = {https://doi.org/10.48550/arXiv.2602.12828},
  eprinttype   = {arXiv},
  eprint       = {2602.12828},
  abstract     = {Predicting future clinical events from longitudinal electronic health records (EHRs) is challenging due to sparse multi-type observations, hierarchical medical vocabularies, and the tendency of large language models (LLMs) to hallucinate when reasoning over long structured histories. We study next-visit event prediction, which aims to forecast a patient's upcoming clinical events from prior visits. We propose GRAIL, a framework that represents longitudinal EHRs through structured geometric modeling and structure-aware retrieval. GRAIL builds a unified clinical graph by combining deterministic coding-system hierarchies with data-driven temporal associations across event types, embeds this graph in hyperbolic space, and summarizes each visit as a probabilistic Central Event to denoise sparse observations. At inference time, it retrieves a structured set of clinically plausible future events that respect hierarchical and temporal progression, and can optionally refine their ranking using an LLM as a constrained reranker. Experiments on MIMIC-IV show that GRAIL consistently improves multi-type next-visit prediction and produces forecasts that are more consistent with clinical hierarchies.}
}

@article{DBLP:journals/corr/abs-2602-12833,
  author       = {Zhan Qu and Michael F{\"{a}}rber},
  title        = {{TRACE:} Temporal Reasoning via Agentic Context Evolution for Streaming Electronic Health Records (EHRs)},
  journal      = {CoRR},
  volume       = {abs/2602.12833},
  year         = {2026},
  url          = {https://doi.org/10.48550/arXiv.2602.12833},
  eprinttype   = {arXiv},
  eprint       = {2602.12833},
  abstract     = {Large language models (LLMs) encode extensive medical knowledge but struggle to apply it reliably to longitudinal patient trajectories, where evolving clinical states, irregular timing, and heterogeneous events degrade performance over time. Existing adaptation strategies typically rely on fine-tuning or retrieval-based augmentation, which introduce computational overhead, privacy constraints, or instability under long contexts. We introduce TRACE (Temporal Reasoning via Agentic Context Evolution), a framework for temporal clinical reasoning with frozen LLMs that explicitly structures and maintains context rather than extending context windows or updating model parameters. TRACE operates through a dual-memory architecture comprising a static Global Protocol that captures institutional clinical rules and a dynamic Individual Protocol that tracks patient-specific state. Four agentic components---Router, Reasoner, Auditor, and Steward---coordinate over this structured memory to support temporal inference and state evolution. The framework maintains bounded inference cost through structured state compression and selectively audits safety-critical clinical decisions. Evaluated on longitudinal clinical event streams from MIMIC-IV, TRACE substantially improves next-event prediction accuracy, protocol adherence, and clinical safety over long-context and retrieval-augmented baselines, while producing interpretable and auditable reasoning traces.}
}

@article{DBLP:journals/corr/abs-2602-12869,
  author       = {Zhan Qu and
                  Michael F{\"{a}}rber},
  title        = {{X-VORTEX:} Spatio-Temporal Contrastive Learning for Wake Vortex Trajectory Forecasting},
  journal      = {CoRR},
  volume       = {abs/2602.12869},
  year         = {2026},
  url          = {https://doi.org/10.48550/arXiv.2602.12869},
  eprinttype   = {arXiv},
  eprint       = {2602.12869},
  abstract     = {Wake vortices are strong, coherent air turbulences generated by aircraft and remain a major safety and capacity challenge in air traffic management. Tracking their movement, weakening, and dissipation over time from LiDAR measurements is difficult because scans are sparse, vortex signatures fade as the flow breaks down under atmospheric turbulence and instabilities, and point-wise annotation is prohibitively expensive. Existing approaches largely treat each scan as an independent, fully supervised segmentation problem, thereby neglecting temporal structure and scaling poorly to the large unlabeled archives available in practice. We present X-VORTEX, a spatio-temporal contrastive learning framework grounded in Augmentation Overlap Theory that learns physics-aware representations from unlabeled LiDAR point cloud sequences. X-VORTEX addresses two central challenges, sensor sparsity and time-varying vortex dynamics, by constructing paired inputs from the same underlying flight event: a weakly perturbed sequence and a strongly augmented counterpart created through temporal subsampling and spatial masking. This training strategy encourages the model to align representations across missing frames and partial observations. Architecturally, a time-distributed geometric encoder extracts per-scan features, while a sequential aggregator models the evolving vortex state across variable-length sequences. Experiments on a real-world dataset containing more than one million LiDAR scans show that X-VORTEX achieves superior vortex center localization while requiring only 1\% of the labeled data needed by supervised baselines, and that its learned representations also support accurate trajectory forecasting.}
}


@inproceedings{HendlFaerber2025QuantumKG,
  author    = {Jonas Hendl and Michael F{\"a}rber},
  title     = {Incorporating Content-based Features into Quantum Knowledge Graph Embeddings},
  booktitle = {AIQxQIA@ECAI},
  year      = {2025},
  url       = {https://ceur-ws.org/Vol-4153/paper2.pdf},
  abstract     = {Link prediction in relational data structures, such as knowledge graphs, plays a crucial role in maintaining up-to-date and accurate information. While classical approaches typically leverage either the graph's connectivity or associated textual descriptions (e.g., labels, definitions), recent quantum models have focused predominantly on structural aspects, leaving the potential of textual information largely unexplored. This paper presents the first quantum link prediction framework that combines both structural and textual modalities. We first generate classical text embeddings, apply dimensionality reduction, and encode them into quantum circuits using two complementary strategies: an Amplitude Encoding Model for high-dimensional fidelity and an Angle Encoding Model optimized for gate efficiency. Experimental results on standard benchmark datasets demonstrate that incorporating textual features in quantum architectures is not only feasible but also enhances the predictive performance of quantum link prediction models.},
  keywords     = {quantum machine learning, link prediction, knowledge graphs, multimodal learning, text embeddings},
 tldr  = {We introduce the first quantum link prediction framework that fuses knowledge-graph structure with text features, using amplitude and angle encoding to inject reduced text embeddings into quantum circuits and improve prediction accuracy.},
 plain = {We teach a quantum model to “read the labels” in a knowledge graph—not just follow the links. By turning short texts into simple signals for a quantum circuit, it predicts missing connections more accurately.},
 pdf = {publications/LinkPred_AIQxQIA_ECAI2025.pdf} 
}


@inproceedings{loehr2026hiddenbias,
  title     = {The Hidden Bias: A Study on Explicit and Implicit Political Stereotypes in Large Language Models},
  author    = {L{\"o}hr, Konrad and Yuan, Shuzhou and F{\"a}rber, Michael},
  booktitle = {EACL},
  year      = {2026},
  url       = {https://arxiv.org/pdf/2510.08236},
  abstract  = {Large Language Models (LLMs) are increasingly integral to information dissemination and decision-making processes. Given their growing societal influence, understanding potential biases, particularly within the political domain, is crucial to prevent undue influence on public opinion and democratic processes. This work investigates political bias and stereotype propagation across eight prominent LLMs using the two-dimensional Political Compass Test (PCT). Initially, the PCT is employed to assess the inherent political leanings of these models. Subsequently, persona prompting with the PCT is used to explore explicit stereotypes across various social dimensions. In a final step, implicit stereotypes are uncovered by evaluating models with multilingual versions of the PCT. Key findings reveal a consistent left-leaning political alignment across all investigated models. Furthermore, while the nature and extent of stereotypes vary considerably between models, implicit stereotypes elicited through language variation are more pronounced than those identified via explicit persona prompting. Interestingly, for most models, implicit and explicit stereotypes show a notable alignment, suggesting a degree of transparency or "awareness" regarding their inherent biases. This study underscores the complex interplay of political bias and stereotypes in LLMs.},
  keywords  = {LLMs, bias, politics, stereotypes},
  tldr = {We measure political leanings and stereotypes in major LLMs and show that language variation can reveal stronger implicit bias than persona prompts.},
  plain = {We run chatbots through a “political compass” quiz and then take a close look at the stereotypes they repeat, especially when we ask the same things in another language.},
  pdf = {publications/Political_Bias_and_Stereotypes_in_LLM_EACL2026.pdf}
}

@inproceedings{Qu2026PoeTone,
  author    = {Zhan Qu and Shuzhou Yuan and Michael F{\"a}rber},
  title     = {PoeTone: A Framework for Constrained Generation of Structured Chinese Songci with LLMs},
  abstract  = {This paper presents a systematic investigation into the constrained generation capabilities of large language models (LLMs) in producing Songci, a classical Chinese poetry form characterized by strict structural, tonal, and rhyme constraints defined by Cipai templates. We first develop a comprehensive, multi-faceted evaluation framework that includes: (i) a formal conformity score, (ii) automated quality assessment using LLMs, (iii) human evaluation, and (iv) classification-based probing tasks. Using this framework, we evaluate the generative performance of 18 LLMs, including 3 proprietary models and 15 open-source models across four families, under five prompting strategies: zero-shot, one-shot, completion-based, instruction-tuned, and chain-of-thought. Finally, we propose a Generate-Critic architecture in which the evaluation framework functions as an automated critic. Leveraging the critic's feedback as a reward signal, we fine-tune three lightweight open-source LLMs via supervised fine-tuning (SFT), resulting in improvements of up to 5.88% in formal conformity. Our findings offer new insights into the generative strengths and limitations of LLMs in producing culturally significant and formally constrained literary texts.},
  booktitle = {AAAI},
  year      = {2026},
  location   = {Singapore},
  url       = {https://doi.org/10.48550/arXiv.2508.02515},
  pdf = {publications/PoeTone_AAAI2026.pdf},
  keywords = {LLMs, constrained generation, Chinese poetry, Songci},
  tldr = {We build a framework for generating classical Chinese Songci that satisfies strict tone and rhyme constraints.},
  plain = {Songci poetry has rules like sheet music, and we teach an AI to write it while staying on the right tones and rhymes, with a strict checker that keeps it honest.},
  selected = {true}
}

@inproceedings{Yuan2025Psycholinguistics,
  title     = {From Monolingual to Bilingual: Investigating Language Conditioning in Large Language Models for Psycholinguistic Tasks},
  author    = {Yuan, Shuzhou and Qu, Zhan and Tawfelis, Mario and F{\"a}rber, Michael},
  booktitle = {AACL},
  year      = {2025},
  location   = {Mumbai, India},
  abstract  = {Large Language Models (LLMs) exhibit strong linguistic capabilities, but little is known about how they encode psycholinguistic knowledge across languages. We investigate whether and how LLMs exhibit human-like psycholinguistic responses under different linguistic identities using two tasks: sound symbolism and word valence. We evaluate two models, Llama-3.3-70B-Instruct and Qwen2.5-72B-Instruct, under monolingual and bilingual prompting in English, Dutch, and Chinese. Behaviorally, both models adjust their outputs based on prompted language identity, with Qwen showing greater sensitivity and sharper distinctions between Dutch and Chinese. Probing analysis reveals that psycholinguistic signals become more decodable in deeper layers, with Chinese prompts yielding stronger and more stable valence representations than Dutch. Our results demonstrate that language identity conditions both output behavior and internal representations in LLMs, providing new insights into their application as models of cross-linguistic cognition.},
  pdf       = {publications/Psycholinguistics_AACL2025.pdf},
  tldr = {We show that prompting an LLM with a specific language identity changes its behavior on psycholinguistic tasks and shifts what its internal layers encode across languages.},
plain = {We show that an AI can “put on” another language like a costume and then answers differently, and even its inner signals shift when it is prompted in a different language.}
}

@inproceedings{yuan2025llmintheloop,
  title     = {LLM in the Loop: Creating the ParaDeHate Dataset for Hate Speech Detoxification},
  author    = {Yuan, Shuzhou and Nie, Ercong and Kouba, Lukas and Kangen, Ashish Yashwanth and Schmid, Helmut and Sch{\"u}tze, Hinrich and F{\"a}rber, Michael},
  booktitle = {AACL},
  year      = {2025},
  url = {https://arxiv.org/pdf/2506.01484},
  abstract = {Detoxification, the task of rewriting harmful language into non-toxic text, has become increasingly important amid the growing prevalence of toxic content online. However, high-quality parallel datasets for detoxification, especially for hate speech, remain scarce due to the cost and sensitivity of human annotation. In this paper, we propose a novel LLM-in-the-loop pipeline leveraging GPT-4o-mini for automated detoxification. We first replicate the ParaDetox pipeline by replacing human annotators with an LLM and show that the LLM performs comparably to human annotation. Building on this, we construct ParaDeHate, a large-scale parallel dataset specifically for hate-speech detoxification. We release ParaDeHate as a benchmark of over 8K hate/non-hate text pairs and evaluate a wide range of baseline methods. Experimental results show that models such as BART, fine-tuned on ParaDeHate, achieve better performance in style accuracy, content preservation, and fluency, demonstrating the effectiveness of LLM-generated detoxification text as a scalable alternative to human annotation.},
  tldr = {We create ParaDeHate, an LLM-in-the-loop dataset for hate-speech detoxification, enabling systematic evaluation of meaning-preserving toxic-text rewriting.},
plain = {We build a large training set that rewrites toxic text into cleaner language without changing the meaning, like running dirty water through a filter and checking the result.}
}

@inproceedings{nie2025decomposed,
  title     = {Decomposed Prompting: Probing Multilingual Linguistic Structure Knowledge in Large Language Models},
  author    = {Nie, Ercong and Yuan, Shuzhou and Ma, Bolei and Schmid, Helmut and F{\"a}rber, Michael and Kreuter, Frauke and Sch{\"u}tze, Hinrich},
  booktitle = {AACL},
  year      = {2025},
  url       = {https://arxiv.org/pdf/2402.18397},
  abstract  = {Probing the multilingual knowledge of linguistic structure in large language models (LLMs), often characterized as sequence labeling, faces challenges with maintaining output templates in current text-to-text prompting strategies. To address this, we introduce a decomposed prompting approach for sequence labeling tasks. Instead of relying on a single text-to-text prompt, our method generates an individual prompt for each token in the input sentence, querying its linguistic label. We evaluate our approach on Universal Dependencies part-of-speech tagging across 38 languages, using both English-centric and multilingual LLMs. Our results show that decomposed prompting outperforms iterative prompting baselines in both effectiveness and efficiency under zero- and few-shot settings. Moreover, our analysis of multilingual performance in English-centric LLMs provides insights into the transferability of linguistic knowledge via multilingual prompting.},
  tldr = {We introduce token-wise decomposed prompting for multilingual sequence labeling, improving accuracy and efficiency for POS tagging across 38 languages.},
plain = {Instead of asking an AI to label a whole sentence in one go, we ask about each word step by step, like a teacher calling roll, and this makes the labels more reliable across languages.}
}

@inproceedings{salsabil2025contextbased,
  title     = {Context-Based URL Classification for Open Access Datasets and Software in Scholarly Documents},
  author    = {Salsabil, Lamia and Obadage, Rochana R. and Banerjee, Bipasha and Abeysinghe, Yasasi Achinthya and Alam, Sawood and F{\"a}rber, Michael and Ingram, William and Fox, Edward and Wu, Jian},
  booktitle = {JCDL},
  year      = {2025},
  abstract  = {This study presents a novel framework for automatically classifying open-access datasets and software (OADS) URLs in scholarly documents. Accurate classification of OADS-URLs is the first step in investigating the availability and preservability of OADS, a crucial step toward open science and computational reproducibility. Our framework, EnSU, leverages an ensemble-based approach to classify OADS-URLs by their citation contexts. The ensemble integrates three models: a Supervised Contrastive Learning model, a SciBERT-based model, and a BertGCN model. Our framework distinguishes the resource types (dataset vs. software) and providers (author vs. third-party). To train and evaluate EnSU, we compiled a dataset, OADS-1K, comprising 1,129 manually annotated sentences containing URLs along with their expanded contexts. Our model outperforms all baseline classifiers, including a large language model-based approach, with the best F1-score of 90%. The dataset and source code are publicly available at: https://github.com/lamps-lab/EnSU/tree/main.},
  tldr      = {We classify URLs in scholarly documents to identify open datasets and software links, enabling large-scale tracking of research artifacts and openness.},
plain = {We build a “link bouncer” for research papers that checks whether a URL really leads to open data or usable software, so open resources are easier to find and count.}
}

@inproceedings{PolitovSJ025,
  author       = {Andrei Politov and
                  Oleh Shkalikov and
                  Ren{\'{e}} J{\"{a}}kel and
                  Michael F{\"{a}}rber},
  title        = {Revisiting Projection-based Data Transfer for Cross-Lingual Named Entity Recognition in Low-Resource Languages},
  booktitle    = {NoDaLiDa},
  location     = {Tallinn, Estonia},
  pages        = {499--507},
  year         = {2025},
  url          = {https://aclanthology.org/2025.nodalida-1.54/},
  abstract     = {Cross-lingual Named Entity Recognition (NER) exploits knowledge transfer across languages to identify and classify named entities, making it particularly valuable for low-resource languages. In this paper, we show that data-based cross-lingual transfer is an effective strategy for cross-lingual NER and can outperform multilingual language models in low-resource settings. We introduce two key enhancements to the annotation projection step. First, we refine word alignments by leveraging back-translation to improve projection accuracy. Second, we propose a novel, formalized projection method that matches source-language entities with candidate entities extracted in the target language. Through extensive experiments on two datasets covering 57 languages, we demonstrate that our approach consistently outperforms existing projection-based methods for low-resource languages. These results highlight the robustness and effectiveness of projection-based data transfer as a strong alternative to model-based approaches for cross-lingual named entity recognition in low-resource scenarios.},
  pdf          = {publications/XNER_NoDaLiDA2025.pdf},
tldr = {We improve projection-based cross-lingual NER using back-translation and a formal matching step, outperforming baselines across 57 low-resource languages.},
plain = {We improve name detection in languages where training material is scarce by transferring labels from other languages and checking again via translation, like carbon paper plus a second copy to catch errors.}
}

@inproceedings{Claim2source,
  title     = {Claim2Source at CheckThat! 2025: Zero-Shot Style Transfer for Scientific Claim-Source Retrieval},
  author    = {Schreieder, Tobias and F{\"a}rber, Michael},
  abstract = {In this paper, we present our participation in the CheckThat!~2025 Task~4b on scientific claim--source retrieval. We systematically investigate the impact of style transfer on retrieving the scientific publication referenced by COVID-19-related tweets. To this end, we apply seven distinct style transfer methods to both claims and sources and evaluate their effects on retrieval performance. The style-transferred variants are assessed across 15 retrieval systems, comprising one sparse, seven dense, and seven hybrid models, by testing all combinations of claim and source styles. The style transfer process is guided by a modular zero-shot prompting template with detailed instructions using a large language model. Our results show that GritLM-7B achieves the best performance without style transfer, indicating strong robustness to informal text. In contrast, most retrieval models, particularly sparse and hybrid approaches, benefit from transforming claims into a more formal writing style. Furthermore, we find that hybrid retrieval models generally outperform dense-only models, highlighting the advantages of combining sparse and dense retrieval paradigms for scientific claim--source retrieval.},
  booktitle = {CLEF},
  year      = {2025},
  pages     = {1203--1216},
  address   = {Madrid, Spain},
  url  = {https://ceur-ws.org/Vol-4038/paper_94.pdf},
  pdf = {publications/Claim2Source_CLEF2025.pdf},
tldr = {We test whether rewriting informal scientific claims in a more formal style improves claim-to-paper retrieval, and analyze when hybrid retrieval helps most.},
plain = {We show that rewriting informal claims into a cleaner, paper-like wording helps find the right scientific source, like using the correct catalog terms when searching a library.}
}

@article{Adib:25,
 author = {Md Mosaddek Hossain Adib and Patrick Matalla and Christoph F\"{u}llner and Shi Li and Elias Giacoumidis and Christian Raack and Ulrich Menne and Michael Straub and Tarek Saier and Christoph Schweikert and Stefan Orf and Martin Gontscharow and Tobias K\"{a}fer and Michael F\"{a}rber and Andr\'{e} Richter and Ren\'{e} Bonk and Sebastian Randel},
 journal = {J. Opt. Commun. Netw.},
 keywords = {Heterodyne detection; Network topology; Networking hardware; Neural networks; Optical networks; Semiconductor optical amplifiers},
 number = {3},
 pages = {221--232},
 publisher = {Optica Publishing Group},
 title = {Optical-access networks for smart sustainable cities: from network architecture to fiber deployment},
 volume = {17},
 month = {Mar},
 year = {2025},
 url = {https://opg.optica.org/jocn/abstract.cfm?URI=jocn-17-3-221},
 doi = {10.1364/JOCN.542368},
 abstract = {With the steadily progressing digitization of our society and the migration into urban areas, digitized and highly connected smart cities have attracted much attention from the research community due to their impact on everyday life, potential for new innovations, and ability to reduce carbon footprints. The versatile applications, which are intended to improve life in cities in various aspects, have one thing in common---they rely on widespread, reliable, and high-performing communication networks. Therefore, optical-access networks will be a crucial part of the smart cities' network infrastructure as they provide cost-effective and high-speed connectivity to antenna sites, residents, enterprises, businesses, and regional data centers in a point-to-multipoint topology. In this article, we address the overall impact of this urban transformation on such networks. We outline our vision of the future smart sustainable city, which will leverage advanced optical-access networks. Subsequently, the physical layer design of optical-access networks is analyzed in the context of point-to-multipoint network topology. This includes a 100-Gbit/s intensity-modulation and direct-detection passive optical network (PON) and a 200-Gbit/s coherent PON utilizing eight-digital subcarrier-based time- and wavelength-division multiplexing and coherent detection. We discuss artificial intelligence-based network monitoring and resource allocation. Next, we provide a techno-economical study for sustainable fiber deployment strategies. Finally, we report the results of a network demonstration for the remote assistance of a connected autonomous vehicle.},
 pdf = {publications/SmartCity_JOCN2025},
tldr = {We outline optical access-network designs for smart cities and discuss AI-based monitoring and cost-aware fiber deployment for sustainable connectivity.},
plain = {This work sketches the “roads and water pipes” of smart cities, explaining how fiber networks and AI monitoring can keep future connectivity fast, stable, and energy-aware.}
}

@inproceedings{Besrour2025SQuAI,
  author    = {Ines Besrour and Jingbo He and Tobias Schreieder and Michael F{\"a}rber},
  title     = {SQuAI: Scientific Question-Answering with Multi-Agent Retrieval-Augmented Generation},
  booktitle = {CIKM},
location = {Seoul, South Korea},
  year      = {2025},
  url       = {https://dl.acm.org/doi/pdf/10.1145/3746252.3761471},
  abstract  = {We present SQuAI, a scalable and trustworthy multi-agent retrieval-augmented generation (RAG) framework for scientific question answering (QA) with large language models (LLMs). SQuAI addresses key limitations of existing RAG systems in the scholarly domain, where complex, open-domain questions demand accurate answers, explicit claims with citations, and retrieval across millions of scientific documents. Built on over 2.3 million full-text papers from arXiv.org, SQuAI employs four collaborative agents to decompose complex questions into sub-questions, retrieve targeted evidence via hybrid sparse-dense retrieval, and adaptively filter documents to improve contextual relevance. To ensure faithfulness and traceability, SQuAI integrates in-line citations for each generated claim and provides supporting sentences from the source documents. Our system improves faithfulness, answer relevance, and contextual relevance by up to +0.088 (12\%) over a strong RAG baseline. We further release a benchmark of 1,000 scientific question-answer-evidence triplets to support reproducibility. With transparent reasoning, verifiable citations, and domain-wide scalability, SQuAI demonstrates how multi-agent RAG enables more trustworthy scientific QA with LLMs.},
  pdf = {publications/SQuAI_CIKM2025.pdf},
keywords = {scientific QA, RAG, multi-agent systems, citations, LLMs},
tldr = {SQuAI answers scientific questions via multi-agent retrieval over millions of papers and produces claim-level citations with supporting evidence sentences.},
plain = {SQuAI works like a team of librarians by splitting a hard question into smaller ones, pulling evidence from millions of papers, and answering with sources that readers can check.}
}

@inproceedings{Shao2025RealE,
  author    = {Chen Shao and Yue Wang and Zhenyi Zhu and Zhanbo Huang and Sebastian P{\"u}tz and Benjamin Sch{\"a}fer and Tobias K{\"a}fer and Michael F{\"a}rber},
  title     = {Real-E: A Foundation Benchmark for Advancing Robust and Generalizable Electricity Forecasting},
  booktitle = {CIKM},
  year      = {2025},
  location   = {Seoul, South Korea},
  url       = {https://doi.org/10.48550/arXiv.2509.05768},
  abstract  = {Energy forecasting is vital for grid reliability and operational efficiency. Although recent advances in time series forecasting have led to progress, existing benchmarks remain limited in spatial and temporal scope and lack multi-energy features. This raises concerns about their reliability and applicability in real-world deployment. To address this, we present the Real-E dataset, covering over 74 power stations across 30+ European countries over a 10-year span with rich metadata. Using Real-E, we conduct an extensive data analysis and benchmark over 20 baselines across various model types. We introduce a new metric to quantify shifts in correlation structures and show that existing methods struggle on our dataset, which exhibits more complex and non-stationary correlation dynamics. Our findings highlight key limitations of current methods and offer a strong empirical basis for building more robust forecasting models.},
  pdf = {publications/Real-E_CIKM2025.pdf},
  keywords = {energy forecasting, time series},
  tldr = {We release Real-E, a large multi-country electricity-forecasting benchmark, and show current methods struggle under non-stationary correlation shifts.},
  plain = {We build a “weather station” for electricity, release a large real-world testbed, and show why models stumble when the grid’s patterns change over time.}
}

@inproceedings{Yuan2025HatefulPersona,
  author    = {Shuzhou Yuan and Ercong Nie and Mario Tawfelis and Helmut Schmid and Hinrich Sch{\"u}tze and Michael F{\"a}rber},
  title     = {Hateful Person or Hateful Model? Investigating the Role of Personas in Hate Speech Detection by Large Language Models},
  booktitle = {PALS@EMNLP},
  year      = {2025},
  url       = {https://doi.org/10.48550/arXiv.2506.08593},
  abstract  = {Hate speech detection is a socially sensitive and inherently subjective task, with judgments often varying based on personal traits. While prior work has examined how socio-demographic factors influence annotation, the impact of personality traits on Large Language Models (LLMs) remains largely unexplored. In this paper, we present the first comprehensive study on the role of persona prompts in hate speech classification, focusing on MBTI-based traits. A human annotation survey confirms that MBTI dimensions significantly affect labeling behavior. Extending this to LLMs, we prompt four open-source models with MBTI personas and evaluate their outputs across three hate speech datasets. Our analysis uncovers substantial persona-driven variation, including inconsistencies with ground truth, inter-persona disagreement, and logit-level biases. These findings highlight the need to carefully define persona prompts in LLM-based annotation workflows, with implications for fairness and alignment with human values.},
  pdf = {publications/PersonaHate_EMNLP2025.pdf},
keywords = {hate speech, personas, MBTI, bias, LLM behavior},
tldr = {We show that persona prompts can substantially change LLM hate-speech labels, raising fairness and reliability concerns for LLM-based moderation and annotation.},
plain = {We show that switching a chatbot’s “persona mask” can change its hate-speech judgments, like getting different verdicts from the same judge in different costumes.}
}

@inproceedings{gruber2024complextempqa,
	title        = {ComplexTempQA: A 100m Dataset for Complex Temporal Question Answering},
	author       = {Raphael Gruber and Abdelrahman Abdallah and Michael Färber and Adam Jatowt},
	booktitle    = {EMNLP},
	location     = {Suzhou, China},
	year         = 2025,
	url = {https://arxiv.org/abs/2406.04866},
	abstract     = {We introduce ComplexTempQA, a large-scale dataset consisting of over 100 million question-answer pairs designed to tackle the challenges in temporal question answering. ComplexTempQA significantly surpasses existing benchmarks in scale and scope. Utilizing Wikipedia and Wikidata, the dataset covers questions spanning over two decades and offers an unmatched scale. We introduce a new taxonomy that categorizes questions as attributes, comparisons, and counting questions, revolving around events, entities, and time periods, respectively. A standout feature of ComplexTempQA is the high complexity of its questions, which demand reasoning capabilities for answering such as across-time comparison, temporal aggregation, and multi-hop reasoning involving temporal event ordering and entity recognition. Additionally, each question is accompanied by detailed metadata, including specific time scopes, allowing for comprehensive evaluation of temporal reasoning abilities of large language models.},
	pdf          = {publications/ComplexTempQA_EMNLP2025.pdf},
    keywords = {temporal QA, dataset, multi-hop reasoning, Wikidata, temporal reasoning},
    tldr = {ComplexTempQA provides 100 million temporal QA pairs that require multi-hop reasoning, enabling realistic evaluation of temporal reasoning in language models.},
    plain = {We create a giant time-travel quiz for AI with 100 million questions about “before”, “after”, and “during”, to see whether models truly keep track of time.}
}

@inproceedings{Popovic2025JEDI,
  author    = {Nicholas Popovic and Michael F{\"a}rber},
  title     = {Extractive Fact Decomposition for Interpretable Natural Language Inference in One Forward Pass},
  booktitle = {EMNLP},
  year      = {2025},
  location   = {Suzhou, China},
  url       = {https://aclanthology.org/2025.emnlp-main.1615.pdf},
  abstract  = {Recent works in Natural Language Inference (NLI) and related tasks, such as automated fact-checking, employ atomic fact decomposition to enhance interpretability and robustness. For this, existing methods rely on resource-intensive generative large language models (LLMs) to perform decomposition. We propose JEDI, an encoder-only architecture that jointly performs extractive atomic fact decomposition and interpretable inference without requiring generative models during inference. To facilitate training, we produce a large corpus of synthetic rationales covering multiple NLI benchmarks. Experimental results demonstrate that JEDI achieves competitive accuracy in distribution and significantly improves robustness out of distribution and in adversarial settings over models based solely on extractive rationale supervision. Our findings show that interpretability and robust generalization in NLI can be realized using encoder-only architectures and synthetic rationales.},
  pdf       = {publications/JEDI_EMNLP2025.pdf},
  keywords  = {NLI, interpretability, fact decomposition, encoder-only},
tldr = {JEDI performs atomic fact decomposition and interpretable NLI in a single encoder-only forward pass, reducing reliance on expensive generative inference at test time.},
plain = {We break long statements into small fact “building blocks” and check them efficiently, like snapping sentences into LEGO pieces so the system can justify true or false.}
}

@inproceedings{Yuan2025GTRex,
  author    = {Shuzhou Yuan and Jingyi Sun and Ran Zhang and Michael F{\"a}rber and Steffen Eger and Pepa Atanasova and Isabelle Augenstein},
  title     = {Graph-Guided Textual Explanation Generation Framework},
  booktitle = {EMNLP},
  year      = {2025},
  address   = {Suzhou, China},
  url       = {https://aclanthology.org/2025.emnlp-main.1494.pdf},
  abstract  = {Natural language explanations (NLEs) are commonly used to provide plausible free-text explanations of a model’s reasoning about its predictions. However, recent work has questioned their faithfulness, as they may not accurately reflect the model’s internal reasoning process regarding its predicted answer. In contrast, highlight explanations--input fragments critical for the model’s predicted answers--exhibit measurable faithfulness. Building on this foundation, we propose G-TEx, a Graph-Guided Textual Explanation Generation framework designed to enhance the faithfulness of NLEs. Specifically, highlight explanations are first extracted as faithful cues reflecting the model’s reasoning logic toward answer prediction. They are subsequently encoded through a graph neural network layer to guide the NLE generation, which aligns the generated explanations with the model’s underlying reasoning toward the predicted answer. Experiments on both encoder-decoder and decoder-only models across three reasoning datasets demonstrate that G-TEx improves NLE faithfulness by up to 12.18\% compared to baseline methods. Additionally, G-TEx generates NLEs with greater semantic and lexical similarity to human-written ones. Human evaluations show that G-TEx can decrease redundant content and enhance the overall quality of NLEs. Our work presents a novel method for explicitly guiding NLE generation to enhance faithfulness, serving as a foundation for addressing broader criteria in NLE and generated text.},
  pdf = {publications/GTRex_EMNLP2025.pdf},
keywords = {explainability, NLE, graph neural networks, faithfulness, LLM},
tldr = {We generate free-text explanations guided by faithful highlight cues encoded with a graph layer, improving explanation faithfulness across reasoning datasets.},
plain = {We make explanations more like a guided tour by having the model point to evidence first and then connect it step by step, instead of telling a nice story after the fact.}
}

@inproceedings{XLLM-ACL2025,
  author    = {Nicholas Popovic and Ashish Kangen and Tim Schopf and Michael F{\"a}rber},
  title     = {In-Context Learning for Information Extraction using Fully Synthetic Demonstrations},
  booktitle = {XLLM@ACL},
  year      = {2025},
  abstract  = {Large, high-quality annotated corpora remain scarce in document-level entity and relation extraction in zero-shot or few-shot settings. In this paper, we present a fully automatic, LLM-based pipeline for synthetic data generation and in-context learning for document-level entity and relation extraction. In contrast to existing approaches that rely on manually annotated demonstrations or direct zero-shot inference, our method combines synthetic data generation with retrieval-based in-context learning, using a reasoning-optimized language model. This allows us to build a high-quality demonstration database without manual annotation and to dynamically retrieve relevant examples at inference time. Based on our approach we produce a synthetic dataset of over 5k Wikipedia abstracts with approximately 59k entities and 30k relation triples. Finally, we evaluate in-context learning performance on the DocIE shared task, extracting entities and relations from long documents in a zero-shot setting. We find that in-context joint entity and relation extraction at document-level remains a challenging task, even for state-of-the-art large language models.},
  keywords  = {information extraction, entity extraction, relation extraction, in-context learning, synthetic data, document-level IE, LLMs},
  location  = {Vienna, Austria},
  pdf       = {publications/DocIE_XLLM-ACL2025.pdf},
  tldr = {We generate synthetic demonstrations and retrieve them at inference to improve document-level entity and relation extraction in low- or zero-shot settings.},
  plain = {We let the AI write its own practice questions and then pull the most useful ones at the right moment, like learning with flashcards you made yourself and picking the cards that really help.}
}

@inproceedings{SDP-ACL2025,
	author = {Tim Schopf, Juraj Vladika, Michael Färber, Florian Matthes},
	title = {Natural Language Inference Fine-tuning for Scientific Hallucination Detection},
	booktitle = {SDP@ACL},
	year = {2025},
	abstract = {Modern generative Large Language Models (LLMs) are capable of generating text that sounds coherent and convincing, but are also prone to producing hallucinations, facts that contradict the world knowledge. Even in the case of Retrieval-Augmented Generation (RAG) systems, where relevant context is first retrieved and passed in the input, the generated facts can contradict or not be verifiable by the provided references. This has motivated SciHal 2025, a shared task that focuses on the detection of hallucinations for scientific content. The two subtasks focused on: (1) predicting whether a claim from a generated LLM answer is entailed, contradicted, or unverifiable by the used references; (2) predicting a fine-grained category of erroneous claims. Our best performing approach used an ensemble of fine-tuned encoder-only ModernBERT and DeBERTa-v3 models for classification. Out of nine competing teams, our approach achieved the first place in sub-task 1 and the second place in sub-task 2.},
	location = {Vienna, Austria},
	pdf = {publications/NLI_Scholarly-Hallucination-Detection_SDP-ACL2025.pdf},
    keywords = {hallucination detection, NLI, scientific text, LLMs},
    tldr = {We fine-tune NLI models to detect scientific hallucinations by checking claims against references, achieving top performance in a shared task.},
plain = {We build a science fact-checker that compares a model’s statements with the sources it cites, like a receipt check that flags items that do not match.}
}

@inproceedings{10.1145/2983323.2983324,
	author = {Zhang, Lei and F\"{a}rber, Michael and Rettinger, Achim},
	title = {XKnowSearch! Exploiting Knowledge Bases for Entity-based Cross-lingual Information Retrieval},
	year = {2016},
	isbn = {9781450340731},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/2983323.2983324},
	doi = {10.1145/2983323.2983324},
	abstract = {In recent years, the amount of entities in large knowledge bases available on the Web has been increasing rapidly, making it possible to propose new ways of intelligent information access. Within the context of globalization, there is a clear need for techniques and systems that can enable multilingual and cross-lingual information access. In this paper, we present XKnowSearch!, a novel entity-based system for multilingual and cross-lingual information retrieval, which supports keyword search and also allows users to influence the search process according to their search intents. By leveraging the multilingual knowledge base on the Web, keyword queries and documents can be represented in their semantic forms, which can facilitate query disambiguation and expansion, and can also overcome the language barrier between queries and documents in different languages.},
	booktitle = {CIKM},
	pages = {2425–2428},
	keywords = {knowledge bases, information retrieval, entity-based, cross-lingual},
	location = {Indianapolis, Indiana, USA},
	pdf = {publications/XKnowSearch_CIKM2016.pdf},
tldr = {XKnowSearch uses knowledge-base entities to disambiguate queries and retrieve documents across languages for entity-based cross-lingual information retrieval.},
plain = {We build a cross-language search tool that uses structured background knowledge like a bilingual map, so you can find relevant texts even when the query and documents use different languages.}
}

@inproceedings{Susanti2025PathToCausality,
	title        = {Paths to Causality: Finding Informative Subgraphs within Knowledge Graphs for Knowledge-Based Causal Discovery},
	author       = {Yuni Susanti and Michael Färber},
	booktitle    = {KDD},
	year         = {2025},
	address      = {Toronto, Canada},
	doi          = {10.1145/3711896.3737076},
	abstract     = {Inferring causal relationships between variable pairs is crucial for understanding multivariate interactions in complex systems. Knowledge-based causal discovery—which involves inferring causal relationships by reasoning over the metadata of variables (e.g., names or textual context)—offers a compelling alternative to traditional methods that rely on observational data. However, existing methods using Large Language Models (LLMs) often produce unstable and inconsistent results, compromising their reliability for causal inference. To address this, we introduce a novel approach that integrates Knowledge Graphs (KGs) with LLMs to enhance knowledge-based causal discovery. Our approach identifies informative metapath-based subgraphs within KGs and further refines their selection using Learning-to-Rank models. The top-ranked subgraphs are then incorporated into zero-shot prompts, improving the effectiveness of LLMs in inferring the causal relationship. Extensive experiments on biomedical and open-domain datasets demonstrate that our method outperforms most baselines by up to 44.8 points in F1 scores, evaluated across diverse LLMs and KGs. Our code and datasets are available on GitHub.},
	pdf          = {publications/Paths_to_Causality_KDD2025.pdf},
    keywords = {causal discovery, knowledge graphs, LLMs, metapaths, ranking},
tldr = {We propose a neurosymbolic method for knowledge-based causal discovery that selects relevant knowledge graph subgraphs to ground LLM prompting.},
plain = {We help AI reason about cause and effect by highlighting the most informative routes through a knowledge map, like handing a detective the best trail of clues.},
selected = {true}
}

@article{DBLP:journals/program/Farber16,
	title        = {Using a semantic wiki for technology forecast and technology monitoring},
	author       = {Michael F{\"{a}}rber},
	year         = {2015},
	journal      = {Program},
	volume       = {50},
	number       = {2},
	pages        = {225--242},
	doi          = {10.1108/PROG-06-2015-0043},
	url          = {https://doi.org/10.1108/PROG-06-2015-0043},
	biburl       = {https://dblp.org/rec/journals/program/Farber16.bib},
    abstract     = {The purpose of this paper is to present extensions of Semantic MediaWiki that support technology forecasting and technology monitoring. Based on requirements elicited from potential end users, namely technology experts, new visualization and analysis components were designed and implemented. The usability and applicability of these components are evaluated through task-based user studies that assess their effectiveness in realistic scenarios. The results show that, although semantic wikis are well suited for knowledge management in industrial contexts due to their user-friendly mechanisms for storing and retrieving information, they have so far rarely been applied to technology forecasting and monitoring. This work demonstrates that the additional requirements of these tasks can be fulfilled and that established techniques for technology analysis can be integrated into Semantic MediaWiki. By doing so, a new application area for Semantic MediaWiki is introduced. The presented tools and techniques provide practical value for technology and innovation management and offer open-source alternatives that can be applied in real-world settings where previously expensive dedicated software solutions were required.},
    keywords     = {semantic wiki, technology forecasting, technology monitoring, knowledge management, innovation management},
    pdf          = {publications/SemWiki_PROG2015.pdf},
tldr = {We extend Semantic MediaWiki with forecasting and monitoring features so organizations can track emerging technologies using structured knowledge and practical workflows.},
plain = {We turn a semantic wiki into a radar for emerging technologies, so teams can collect and compare new signals instead of losing them in scattered notes.}
}

@article{DBLP:journals/ws/ZhangTRFMD17,
	title        = {The xLiMe system: Cross-lingual and cross-modal semantic annotation, search and recommendation over live-TV, news and social media streams},
	author       = {Lei Zhang and Andreas Thalhammer and Achim Rettinger and Michael F{\"{a}}rber and Aditya Mogadala and Ronald Denaux},
	year         = 2017,
	journal      = {Web Semantics},
	volume       = {46-47},
	pages        = {20--30},
	doi          = {10.1016/J.WEBSEM.2017.03.002},
	url          = {https://doi.org/10.1016/j.websem.2017.03.002},
    abstract     = {Modern web search engines still exhibit several limitations, including the lack of search term disambiguation, the inability to handle multilingual search terms within a single query, restrictions that require retrieved media items to be in the same language as the query, and limited integration of search results across live streams of heterogeneous media sources such as television, online news, and social media. The system presented in this paper addresses these challenges by combining a media stream processing architecture with cross-lingual and cross-modal semantic annotation, search, and recommendation capabilities. All components of the system were developed within the xLiMe project.},
    keywords     = {semantic annotation, cross-lingual search, cross-modal search, media stream processing, recommendation systems},
    pdf          = {publications/xLiMe-System_JWS2017.pdf},
tldr = {We enable cross-lingual and cross-media semantic annotation, search, and recommendation over live TV, news, and social media streams.},
plain = {We build a kind of “universal remote” for information streams, linking TV, news, and social media across languages so topics can be followed beyond language barriers.}
}

@article{DBLP:journals/semweb/FarberMH18,
	title        = {A Linked Data Wrapper for CrunchBase},
	author       = {Michael F{\"{a}}rber and Carsten Menne and Andreas Harth},
	year         = 2018,
	journal      = {Semantic Web},
	volume       = 9,
	number       = 4,
	pages        = {505--515},
	doi          = {10.3233/SW-170278},
	url          = {https://doi.org/10.3233/SW-170278},
    abstract     = {CrunchBase is a database of startups and technology companies that can be searched, browsed, and edited via a web interface and is also accessible through an entity-centric HTTP API providing data in JSON format. In this paper, we present a wrapper for the CrunchBase API that exposes the data as Linked Data. The wrapper provides schema-level links to established vocabularies such as schema.org, Friend-of-a-Friend, and Vocabulary-of-a-Friend, as well as entity-level links to DBpedia for organization entities. We further describe how the RDF data can be harvested to create a local copy for advanced processing and querying beyond the capabilities of the original CrunchBase API. Finally, we outline several use cases in which both the Linked Data API for CrunchBase and the crawled CrunchBase RDF dataset have been applied in subsequent research.},
    pdf          = {publications/CrunchBaseWrapper_SWJ2017.pdf},
    keywords     = {Linked Data, APIs, RDF, companies, data integration},
tldr = {We provide Crunchbase as Linked Data, enabling richer integration and querying of startup, people, and investment data with web standards.},
plain = {We publish Crunchbase as Linked Data, turning startup and investment information into a connected map that is easier to combine, query, and analyze.}
}

@article{FarberBMR18,
	title        = {Linked Data Quality of DBpedia, Freebase, OpenCyc, Wikidata, and {YAGO}},
	author       = {Michael F{\"{a}}rber and Frederic Bartscherer and Carsten Menne and Achim Rettinger},
	year         = 2018,
	journal      = {Semantic Web},
	volume       = 9,
	number       = 1,
	pages        = {77--129},
	doi          = {10.3233/SW-170275},
	url          = {https://doi.org/10.3233/SW-170275},
	abstract     = {In recent years, several large, cross-domain, and openly available knowledge graphs (KGs) have been created, including DBpedia, Freebase, OpenCyc, Wikidata, and YAGO. Despite their widespread use, these knowledge graphs have not yet been subjected to a comprehensive comparative analysis. In this survey, we introduce a set of data quality criteria for systematically analyzing knowledge graphs and apply these criteria to compare the aforementioned KGs. In addition, we propose a framework to support the selection of the most suitable knowledge graph for a given application scenario.},
	keywords     = {knowledge graphs, linked data quality, data quality assessment, DBpedia, Wikidata},
	pdf          = {publications/KG-Comparison_SWJ2017.pdf},
    tldr = {We compare major knowledge graphs using a systematic data-quality framework and help practitioners choose the right graph for their application needs.},
plain = {We create a consumer-style test report for major knowledge graphs, comparing data quality so developers can choose the right “map of the world” for their use case.},
selected = {true}
}

@article{DBLP:journals/corr/abs-1809-11099,
	title        = {Which Knowledge Graph Is Best for Me?},
	author       = {Michael F{\"{a}}rber and Achim Rettinger},
	year         = 2018,
	journal      = {CoRR},
	volume       = {abs/1809.11099},
	url          = {http://arxiv.org/abs/1809.11099},
	eprinttype   = {arXiv},
	eprint       = {1809.11099},
	abstract     = {In recent years, DBpedia, Freebase, OpenCyc, Wikidata, and YAGO have emerged as prominent large, cross-domain, and freely available knowledge graphs. Despite their widespread adoption, these knowledge graphs are difficult to compare within specific application settings, making it challenging for researchers and developers to select the most suitable one for their needs. In a prior in-depth survey, we defined and applied a set of data quality criteria to systematically analyze these knowledge graphs and proposed a decision-support framework for identifying the most appropriate KG for a given context. In this paper, we aim to lower the barrier to accessing these results by presenting simplified rules that map individual data quality requirements to specific knowledge graphs. This work is intended as a complement to, rather than a replacement for, the previously introduced decision-support framework, to which we refer readers seeking a well-informed selection process.},
	keywords     = {knowledge graphs, decision support, data quality, linked data, KG selection},
	pdf          = {publications/Which_KG_arXiv2018.pdf},
tldr = {We translate knowledge graph quality analysis into practical selection rules so users can pick a knowledge graph that matches their data quality requirements.},
plain = {We offer a practical “which map should I buy?” guide for knowledge graphs, turning quality checks into simple rules that help people choose.}
}

@article{DBLP:journals/corr/abs-1907-08671,
	title        = {Linked Crunchbase: {A} Linked Data {API} and {RDF} Data Set About Innovative Companies},
	author       = {Michael F{\"{a}}rber},
	year         = 2019,
	journal      = {CoRR},
	volume       = {abs/1907.08671},
	url          = {http://arxiv.org/abs/1907.08671},
	doi          = {10.48550/arXiv.1907.08671},
	eprinttype   = {arXiv},
	eprint       = {1907.08671},
	biburl       = {https://dblp.org/rec/journals/corr/abs-1907-08671.bib},
	abstract     = {Crunchbase is an online platform that aggregates information about startups and technology companies, including attributes and relationships among companies, people, and investments. A large portion of the data contained in Crunchbase is not available from other sources, making it a unique resource. In this paper, we present an approach for bringing Crunchbase data to the Web of Data, enabling its use in a machine-readable RDF format. We first describe the development and hosting of a Linked Data API for Crunchbase, including the integration of \texttt{sameAs} links to external data sources. We then outline our method for crawling RDF data via this API to construct a custom Crunchbase RDF knowledge graph. The resulting dataset comprises over 347 million triples, including 781{,}000 people, 659{,}000 organizations, and 343{,}000 investments. The Crunchbase Linked Data API is publicly available at \url{http://linked-crunchbase.org/}.},
	keywords     = {linked data, RDF, knowledge graphs, startups, innovation data},
	pdf          = {publications/Crunchbase_arXiv2019.pdf},
tldr = {We provide a Linked Data API and RDF dataset for Crunchbase, enabling machine-readable analytics of startups, organizations, people, and investments.},
plain = {We publish Crunchbase as a web-friendly data stream, like adding a spout so machines can “drink” structured startup information.}
}

@article{DBLP:journals/jodl/FarberJ20,
	title        = {Citation Recommendation: Approaches and Datasets},
	author       = {Michael F{\"{a}}rber and Adam Jatowt},
	year         = 2020,
	journal      = {Int. J. Digit. Libr.},
	volume       = 21,
	number       = 4,
	pages        = {375--405},
	doi          = {10.1007/S00799-020-00288-2},
	url          = {https://doi.org/10.1007/s00799-020-00288-2},
	abstract     = {Citation recommendation addresses the task of recommending relevant citations for a given text. Owing to the rapid growth of published scientific literature on the one hand and the necessity of citing the most appropriate works when authoring scientific texts on the other hand, citation recommendation has emerged as an important research area. In recent years, numerous approaches and evaluation datasets have been proposed. However, to the best of our knowledge, no comprehensive literature survey has explicitly focused on citation recommendation. In this article, we provide a thorough introduction to automatic citation recommendation research. We then present an overview of existing approaches and datasets, identifying their differences and commonalities across multiple dimensions. Finally, we discuss evaluation methods, highlight general challenges in evaluation, and outline ways to address them. While we restrict our survey to citation recommendation for scientific publications, as this document type has been studied most extensively, many of the observations and discussions are also applicable to other text types, such as news articles and encyclopedic content.},
	keywords     = {citation recommendation, scholarly search, literature review, datasets, information retrieval},
	pdf          = {publications/CiteRecSurvey_IJDL2020.pdf},
tldr = {We survey citation recommendation methods and datasets and highlight evaluation pitfalls and open challenges for assisting scientific writing.},
plain = {This survey explains citation recommendation, like a GPS for references, and summarizes which datasets and tests are needed before such tools deserve trust.},
selected = {true}
}

@article{DBLP:journals/scientometrics/SaierF20,
	title        = {unarXive: A Large Scholarly Dataset With Publications’ Full Text, Annotated In-Text Citations, and Links to Metadata},
	author       = {Tarek Saier and Michael F{\"{a}}rber},
	year         = 2020,
	journal      = {Scientometrics},
	volume       = 125,
	number       = 3,
	pages        = {3085--3108},
	doi          = {10.1007/S11192-020-03382-Z},
	url          = {https://doi.org/10.1007/s11192-020-03382-z},
	biburl       = {https://dblp.org/rec/journals/scientometrics/SaierF20.bib},
	abstract     = {In recent years, scholarly datasets have been employed for a wide range of tasks, including paper recommendation, citation recommendation, citation context analysis, and citation context-based document summarization. The evaluation of methods for these tasks, as well as their applicability in real-world settings, critically depends on the underlying datasets. However, existing scholarly datasets exhibit several limitations. In this paper, we introduce a new dataset comprising all publications from all scientific disciplines available on arXiv.org. In addition to providing the full plain text of papers, in-text citations are annotated using global identifiers. Moreover, both citing and cited publications are linked to the Microsoft Academic Graph, enabling access to rich bibliographic metadata. The resulting dataset contains over one million documents and 29.2 million citation contexts. Freely available for research purposes, this dataset supports more robust evaluation of paper-based and citation context-based approaches and also enables novel analyses of in-text citations, as demonstrated prototypically in this work.},
	keywords     = {scholarly datasets, in-text citations, citation analysis, scientific corpora, metadata},
	pdf          = {publications/unarXive_Scientometrics2020.pdf},
tldr = {We present unarXive, a large-scale arXiv full-text corpus with precisely linked in-text citations and rich metadata, empowering citation-aware, transparent, and reproducible scholarly NLP.},
plain = {We build a large “paper warehouse” where citations inside arXiv texts are tagged and linked, so citation analyses need far less manual cleanup.}
}

@article{FarberL21,
	title        = {The Data Set Knowledge Graph: Creating a Linked Open Data Source for Data Sets},
	author       = {Michael F{\"{a}}rber and David Lamprecht},
	year         = 2021,
	journal      = {Quant. Science Studies},
	volume       = 2,
	number       = 4,
	pages        = {1324--1355},
	abstract     = {Several scholarly knowledge graphs have been proposed to model and analyze the academic landscape; however, despite the remarkable increase in available data sets in recent years, existing knowledge graphs primarily focus on associated entities such as publications rather than on data sets themselves, and publicly available data set knowledge graphs do not systematically contain links to the publications in which data sets are mentioned. In this paper, we present an approach for constructing an RDF knowledge graph that fulfills these criteria. Our data set knowledge graph, DSKG, is publicly available and contains metadata of data sets across all scientific disciplines. To ensure high data quality, we identify suitable raw data set collections, establish links between data sets and publications modeled in the Microsoft Academic Knowledge Graph that mention these data sets, and address author name ambiguity by developing and evaluating an author name disambiguation method that enriches the knowledge graph with links to ORCID. Overall, the knowledge graph contains more than 2,000 data sets with associated properties and 814,000 links to 635,000 scientific publications, enabling a variety of use cases including advanced data set search systems and new ways of measuring and rewarding data set provisioning.},
	keywords     = {knowledge graphs, research datasets, linked open data, metadata, scholarly infrastructure},
	pdf          = {publications/DSKG_QSS2021.pdf},
tldr = {We introduce the Data Set Knowledge Graph (DSKG), the first large-scale linked open dataset that connects datasets to the scholarly papers that mention them, enabling better dataset discovery and making data contributions transparent and measurable.},
plain = {We link datasets to the papers that mention them, creating a map that helps people find data faster and gives data creators clearer credit.}
}

@article{abs-2111-05097,
	title        = {Cross-Lingual Citations in English Papers: {A} Large-Scale Analysis of Prevalence, Usage, and Impact},
	author       = {Tarek Saier and Michael F{\"{a}}rber and Tornike Tsereteli},
	year         = 2021,
	journal      = {International Journal on Digital Libraries},
	url          = {https://arxiv.org/abs/2111.05097},
	abstract     = {Citation information in scholarly data is an important source of insight into the reception of publications and scholarly discourse, yet the outcomes of citation analyses and the applicability of citation-based machine learning approaches heavily depend on data completeness. A key shortcoming of current scholarly data is the frequent exclusion of non-English publications or the absence of language metadata, which has limited the study of cross-lingual citations. In this paper, we present an analysis of cross-lingual citations based on over one million English-language papers spanning three scientific disciplines and three decades, examining differences across cited languages and disciplines, temporal trends, and the usage characteristics and impact of cross-lingual citations. Our findings include an increasing rate of citations to Chinese-language publications, a predominance of citations to local non-English languages, and consistency in citation intent between cross-lingual and monolingual citations. To facilitate further research, we make the collected data and source code publicly available.},
	keywords     = {cross-lingual citations, citation analysis, multilingual scholarly communication, bibliometrics},
	pdf          = {publications/Cross-lingual_Citations_IJDL2021.pdf},
tldr = {We analyze cross-lingual citations at scale to quantify prevalence and impact, showing how language affects scholarly visibility and citation behavior.},
plain = {We trace citations across languages like trade routes on a map, showing which languages get noticed and cited in English scientific writing.}
}

@article{WangJFY21,
	title        = {Improving Question Answering for Event-focused Questions in Temporal Collections of News Articles},
	author       = {Jiexin Wang and Adam Jatowt and Michael F{\"{a}}rber and Masatoshi Yoshikawa},
	year         = 2021,
	journal      = {Information Retrieval Journal},
	volume       = 24,
	number       = 1,
	pages        = {29--54},
	url          = {https://doi.org/10.1007/s10791-020-09387-9},
	abstract     = {Temporal collections of news articles contain large numbers of accurate and time-aligned documents that are valuable for understanding past events, yet access to such archives remains difficult for average users due to their size and complexity. This work addresses the task of machine reading at scale on long-term news archives, motivated by the observation that questions over news collections are typically event-centric and exhibit strong temporal characteristics. We propose a large-scale question answering model specifically designed for long-term news article collections, incorporating a dedicated module for re-ranking articles using temporal information from multiple perspectives. Experimental results demonstrate that the proposed model outperforms existing question answering systems, primarily due to the temporal re-ranking module’s ability to identify more relevant documents.},
	keywords     = {question answering, temporal information retrieval, news archives, event-centric QA},
	pdf          = {publications/ImprovingQA_IRJ2021.pdf},
tldr = {We improve question answering over long-term news archives with temporal reranking, helping users retrieve relevant evidence for event-centric questions.},
plain = {We make question answering over news archives time-aware, like a historian who knows which year to search when the question forgets to say it.}
}

@article{abs-2112-00160,
	title        = {Towards Full-Fledged Argument Search: {A} Framework for Extracting and Clustering Arguments from Unstructured Text},
	author       = {Michael F{\"{a}}rber and Anna Steyer},
	year         = 2021,
	journal      = {CoRR},
	volume       = {abs/2112.00160},
	url          = {https://arxiv.org/abs/2112.00160},
	eprinttype   = {arXiv},
	eprint       = {2112.00160},
	abstract     = {Argument search aims at identifying arguments in natural language texts, a task that has traditionally been addressed through a combination of keyword search and sentence- or document-level argument identification. However, existing frameworks often cover only specific components of argument search and fail to address key challenges, including argument–query matching for differently framed topics, identification of multi-sentence arguments, and topical clustering of retrieved arguments. In this paper, we propose a unified framework to overcome these limitations by combining keyword search with precomputed topic clusters for argument–query matching, introducing a novel sentence-level sequence-labeling approach for multi-sentence argument identification, and presenting aggregated arguments through topic-aware argument clustering. Experiments on several real-world debate data sets show that density-based clustering algorithms such as HDBSCAN are well suited for argument–query matching, that our BiLSTM-based sequence-labeling approach achieves a macro F1 score of 0.71, and that while fine-grained subtopic clustering of arguments remains challenging, it is a promising direction for further exploration.},
	keywords     = {argument mining, argument search, text clustering, information retrieval},
	pdf          = {publications/Towards_Full-Fledged_Argument_Search_arXiv2021.pdf},
tldr = {We propose a unified argument-search framework that extracts and clusters arguments from text for more comprehensive debate analysis.},
plain = {We build a machine that mines arguments from messy text and sorts them into clusters, like turning a chaotic debate into neat stacks of note cards.}
}

@article{abs-2112-00859,
	title        = {Are Investors Biased Against Women? Analyzing How Gender Affects Startup Funding in Europe},
	author       = {Michael F{\"{a}}rber and Alexander Klein},
	year         = 2021,
	journal      = {CoRR},
	volume       = {abs/2112.00859},
	url          = {https://arxiv.org/abs/2112.00859},
	eprinttype   = {arXiv},
	eprint       = {2112.00859},
	abstract     = {Raising capital from investors is a central challenge for startups, making it crucial for founders to understand whether and how gender bias affects early-stage funding decisions. While prior gender studies have predominantly focused on the US market, this paper provides a more comprehensive analysis of gender bias in European early-stage startup funding. We examine European startups listed on Crunchbase using Semantic Web technologies and analyze the relationship between the share of female founders in a founding team and the amount of funding raised. Our results show that a higher relative share of female founders has a negative impact on funding amounts and that founder characteristics influence funding outcomes differently based on gender. Furthermore, we find that gender bias is less pronounced for serial founders with prior entrepreneurial experience, as female founders benefit three times more than male founders from having previously founded a startup. Overall, our findings indicate that gender bias exists and should be considered in the context of startup funding.},
	keywords     = {gender bias, startup funding, innovation studies, linked data, empirical analysis},
	pdf          = {publications/CrunchBias_2021.pdf},
tldr = {We analyze European startup funding data and quantify how team gender composition relates to funding outcomes, providing evidence of structural bias risks.},
plain = {We analyze European startup funding like checking a pipeline, asking whether team gender composition relates to how money flows and where bias may appear.}
}

@article{DBLP:journals/qss/FarberA22,
	title        = {The Microsoft Academic Knowledge Graph enhanced: Author name disambiguation, publication classification, and embeddings},
	author       = {Michael F{\"{a}}rber and Lin Ao},
	year         = 2022,
	journal      = {Quant. Science Studies},
	volume       = 3,
	number       = 1,
	pages        = {51--98},
	doi          = {10.1162/QSS\_A\_00183},
	url          = {https://doi.org/10.1162/qss\_a\_00183},
    abstract  = {Although several large knowledge graphs have been proposed in the scholarly field, such graphs are limited with respect to several data quality dimensions such as accuracy and coverage. In this article, we present methods for enhancing the Microsoft Academic Knowledge Graph (MAKG), a recently published large-scale knowledge graph containing metadata about scientific publications and associated authors, venues, and affiliations. Based on a qualitative analysis of the MAKG, we address three aspects. First, we adopt and evaluate unsupervised approaches for large-scale author name disambiguation. Second, we develop and evaluate methods for tagging publications by their discipline and by keywords, facilitating enhanced search and recommendation of publications and associated entities. Third, we compute and evaluate embeddings for all 239 million publications, 243 million authors, 49,000 journals, and 16,000 conference entities in the MAKG based on several state-of-the-art embedding techniques. Finally, we provide statistics for the updated MAKG. Our final MAKG is publicly available at https://makg.org and can be used for the search or recommendation of scholarly entities, as well as enhanced scientific impact quantification.},
	pdf = {publications/MAKG+_QSS2022.pdf},
    keywords = {MAKG, author name disambiguation},
tldr = {We enhance a massive scholarly knowledge graph with improved author disambiguation, publication classification, and embeddings to improve search and analytics at scale.},
plain = {We clean and enrich a huge catalog of scientific papers, like fixing name tags and adding smart labels so large-scale search works more reliably.}
}
@inproceedings{faerber2023linked,
	title        = {Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph},
	author       = {Michael Färber and David Lamprecht},
	year         = 2023,
	booktitle    = {ISWC},
	location      = {Athens, Greece},
	url          = {https://arxiv.org/abs/2310.20475},
    abstract  = {In this paper, we introduce Linked Papers With Code (LPWC), an RDF knowledge graph that provides comprehensive, current information about almost 400,000 machine learning publications. This includes the tasks addressed, the datasets utilized, the methods implemented, and the evaluations conducted, along with their results. Compared to its non-RDF-based counterpart Papers With Code, LPWC not only translates the latest advancements in machine learning into RDF format, but also enables novel ways for scientific impact quantification and scholarly key content recommendation. LPWC is openly accessible at this URL and is licensed under CC-BY-SA 4.0. As a knowledge graph in the Linked Open Data cloud, we offer LPWC in multiple formats, from RDF dump files to a SPARQL endpoint for direct web queries, as well as a data source with resolvable URIs and links to the data sources SemOpenAlex, Wikidata, and DBLP. Additionally, we supply knowledge graph embeddings, enabling LPWC to be readily applied in machine learning applications.},
	pdf = {publications/LPWC_ISWC2023.pdf},
    keywords  = {Knowledge Graphs, RDF, Machine Learning, Scholarly Data, Linked Open Data, Knowledge Graph Embeddings,Papers With Code, LPWC},
tldr = {LPWC turns the ML landscape into a machine-queryable knowledge graph linking papers to tasks, datasets, methods, and results for semantic search and analysis.},
plain = {LPWC turns machine-learning research into a structured “periodic table” of papers, tasks, datasets, and results that can be searched and connected.}
}

@inproceedings{faerber2023semopenalex,
	title        = {SemOpenAlex: The Scientific Landscape in 26 Billion RDF Triples},
	author       = {Michael Färber and David Lamprecht and Johan Krause and Linn Aung and Peter Haase},
	year         = 2023,
	booktitle    = {ISWC},
	address      = {Athens, Greece},
	url          = {https://arxiv.org/abs/2308.03671},
    abstract  = {We present SemOpenAlex, an extensive RDF knowledge graph that contains over 26 billion triples about scientific publications and their associated entities, such as authors, institutions, journals, and concepts. SemOpenAlex is licensed under CC0, providing free and open access to the data. We offer the data through multiple channels, including RDF dump files, a SPARQL endpoint, and as a data source in the Linked Open Data cloud, complete with resolvable URIs and links to other data sources. Moreover, we provide embeddings for knowledge graph entities using high-performance computing. SemOpenAlex enables a broad range of use-case scenarios, such as exploratory semantic search via our website, large-scale scientific impact quantification, and other forms of scholarly big data analytics within and across scientific disciplines. Additionally, it enables academic recommender systems, such as recommending collaborators, publications, and venues, including explainability capabilities. Finally, SemOpenAlex can serve for RDF query optimization benchmarks, creating scholarly knowledge-guided language models, and as a hub for semantic scientific publishing.},
	pdf = {publications/SemOpenAlex_ISWC2023.pdf},
    keywords  = {Scholarly Data, Linked Open Data, Knowledge Graph},
tldr = {We release SemOpenAlex, a scholarly knowledge graph with 26 billion triples, dumps, SPARQL access, and embeddings, enabling large-scale semantic science analytics and search.},
plain = {SemOpenAlex is an open “Google Maps for science”, built as a connected map of papers and authors so others can navigate research at web scale.},
selected = {true}
}

@article{DBLP:journals/scientometrics/FarberCY23,
	title        = {Biases in Scholarly Recommender Systems: Impact, Prevalence, and Mitigation},
	author       = {Michael F{\"{a}}rber and Melissa Coutinho and Shuzhou Yuan},
	year         = 2023,
	journal      = {Scientometrics},
	volume       = 128,
	number       = 5,
	pages        = {2703--2736},
	doi          = {10.1007/S11192-023-04636-2},
	url          = {https://doi.org/10.1007/s11192-023-04636-2},
    abstract  = {With the remarkable increase in the number of scientific entities such as publications, researchers, and scientific topics, and the associated information overload in science, academic recommender systems have become increasingly important for millions of researchers and science enthusiasts. However, it is often overlooked that these systems are subject to various biases. In this article, we first break down the biases of academic recommender systems and characterize them according to their impact and prevalence. In doing so, we distinguish between biases originally caused by humans and biases induced by the recommender system. Second, we provide an overview of methods that have been used to mitigate these biases in the scholarly domain. Based on this, third, we present a framework that can be used by researchers and developers to mitigate biases in scholarly recommender systems and to evaluate recommender systems fairly. Finally, we discuss open challenges and possible research directions related to scholarly biases.},
	pdf = {publications/ScholarBias_Scientometrics2023.pdf},
    keywords  = {Scholarly Data, Bias, Recommender Systems, Mitigation, Survey},
    tldr = {We measure biases in scholarly recommender systems and discuss mitigation strategies to support fairer literature discovery under severe paper overload.},
    plain = {We show that literature recommenders can have blind spots, like a librarian who keeps pointing to the same shelves, and we outline how to measure and reduce this effect.}
}

@article{Straub2023OTDRDetection,
  title        = {AI-Based OTDR Event Detection, Classification and Assignment to ODN Branches in Passive Optical Networks},
  author       = {Straub, Michael and Saier, T. and Reber, J. and Borkowski, R. and Li, S. and Richter, A. and F{\"a}rber, Michael and K{\"a}fer, T. and Bonk, R.},
  journal      = {Journal of Optical Communications and Networking},
  year         = {2023},
  doi          = {10.1049/icp.2023.2469},
  abstract     = {An AI-supported monitoring concept is demonstrated allowing detection and classification of events on OTDR traces with high precision and recall for application on a PON optical distribution network. We can also associate events with ODN branches by using deployment data of the PON topology.},
  keywords     = {OTDR, Passive Optical Networks, Optical Distribution Networks, AI-based Monitoring, Event Detection, Fiber Optic Sensing},
  pdf = {publications/OTDR_ECOC2023.pdf},
tldr = {We detect and classify events in optical networks from OTDR traces and assign faults to network branches, improving reliability of passive optical networks.},
plain = {We listen to echoes in fiber cables like sonar and use machine learning to spot and locate faults, improving how networks can be monitored.}
}

@inproceedings{aydin2024assessing,
	title        = {Assessing Privacy Policies with AI: Ethical, Legal, and Technical Challenges},
	author       = {Irem Aydin and Hermann Diebel-Fischer and Vincent Freiberger and Julia Möller-Klapperich and Erik Buchmann and Michael Färber and Anne Lauber-Rönsberg and Birte Platow},
	year         = 2024,
	booktitle    = {AISyS},
	url          = {https://arxiv.org/abs/2410.08381},
	abstract     = {The growing use of Machine Learning and Artificial Intelligence (AI), particularly Large Language Models (LLMs) like OpenAI’s GPT series, leads to disruptive changes across organizations. At the same time, there is a growing concern about how organizations handle personal data. Thus, privacy policies are essential for transparency in data processing practices, enabling users to assess privacy risks. However, these policies are often long and complex. This might lead to user confusion and consent fatigue, where users accept data practices against their interests, and abusive or unfair practices might go unnoticed. LLMss can be used to assess privacy policies for users automatically. In this interdisciplinary work, we explore the challenges of this approach in three pillars, namely technical feasibility, ethical implications, and legal compatibility of using LLMs to assess privacy policies. Our findings aim to identify potential for future research, and to foster a discussion on the use of LLM technologies for enabling users to fulfil their important role as decision-makers in a constantly developing AI-driven digital economy.},
	pdf = {publications/Privacy_Policies_AI_AISyS2024.pdf},
    keywords = {Ethics, Legal, Privacy, Philosophy},
tldr = {We analyze technical feasibility plus ethical and legal risks of using LLMs to assess privacy policies.},
plain = {We ask whether AI can read privacy policies like a magnifying glass for fine print and we lay out the technical, ethical, and legal pitfalls.}
}

@article{OTDRJOCN2024,
	title        = {{ML} approaches for {OTDR} diagnoses in passive optical networks - event detection and classification: ways for {ODN} branch assignment},
	author       = {Michael Straub and Johannes Reber and Tarek Saier and Robert Borkowski and Shi Li and Dmitry Khomchenko and Andr{\'{e}} Richter and Michael F{\"{a}}rber and Tobias K{\"{a}}fer and Ren{\'{e}} Bonk},
	year         = 2024,
	journal      = {J. Opt. Commun. Netw.},
	volume       = 16,
	number       = 7,
	pages        = 43,
	doi          = {10.1364/JOCN.516659},
	url          = {https://doi.org/10.1364/jocn.516659},
	biburl       = {https://dblp.org/rec/journals/jocnet/StraubRSBLKRFKB24.bib},
	abstract = {We introduce and demonstrate an ML-supported diagnostic concept for detecting and classifying events on OTDR traces in a PON optical distribution network. By incorporating PON deployment data, events can also be associated with specific ODN branches. We analyze an ensemble classifier and neural networks, and evaluate the use of synthetic OTDR-like traces alongside measured data for training. In our proof-of-concept, an ensemble classifier achieves 98\% precision and 95\% recall on measured OTDR traces, including successful mapping to ODN branches or groups of branches. On emulated data, we obtain an average precision of 70\% and an average recall of 91\%.},
	pdf = {publications/OTDR_JOCN2024.pdf},
    keywords = {OTDR, PON, Machine Learning, Event Detection, Fiber Monitoring},
tldr = {We improve ML-based OTDR diagnostics using measured and synthetic traces and map events to network branches for practical fiber monitoring.},
plain = {We improve fiber fault diagnosis by training on both real and simulated signals, like training a mechanic on real cars and realistic simulators.}
}

@article{CollabScientometrics2024,
	title        = {Benefits of international collaboration in computer science: a case study of China, the European Union, and the United States},
	author       = {Alberto G{\'{o}}mez{-}Esp{\'{e}}s and Michael F{\"{a}}rber and Adam Jatowt},
	year         = 2024,
	journal      = {Scientometrics},
	volume       = 129,
	number       = 2,
	pages        = {1155--1171},
	doi          = {10.1007/S11192-023-04902-3},
	url          = {https://doi.org/10.1007/s11192-023-04902-3},
abstract = {Co-authored publications can yield benefits such as additional expertise, increased funding opportunities, and enhanced research impact. China, the European Union, and the United States have collaborated extensively in Computer Science over the past decades, though the scale and effects of these collaborations have changed over time. In this paper, we analyze 31 years of Computer Science publications from these three regions, examining co-authorship patterns, citation impact, and research topics. Our results show that China's growing emphasis on Computer Science has made it the most productive region in recent years, and collaborations with the EU and the US have increased its citation rates. Conversely, the EU and the US have benefited from China's expanding research output, resulting in a higher volume of joint publications.},
	pdf = {publications/Collaboration_Scientometrics2024.pdf},
keywords = {International Collaboration, Co-authorship, Citation Impact, China, EU, US},
tldr = {We quantify how international collaboration in computer science relates to productivity and citation impact across China, the EU, and the United States.},
plain = {We map scientific collaboration like flight routes and study how cross-border co-authoring relates to productivity and impact over decades.}
}

@article{DBLP:journals/scientometrics/FarberT24,
	title        = {Analyzing the Impact of Companies on {AI} Research Based on Publications},
	author       = {Michael F{\"{a}}rber and Lazaros Tampakis},
	year         = 2023,
	journal      = {Scientometrics},
	volume       = 129,
	number       = 1,
	pages        = {31--63},
	doi          = {10.1007/S11192-023-04867-3},
	url          = {https://doi.org/10.1007/S11192-023-04867-3},
    abstract     = {Artificial Intelligence (AI) is one of the most momentous technologies of our time. Thus, it is of major importance to know which stakeholders influence AI research. Besides researchers at universities and colleges, researchers in companies have hardly been considered in this context. In this article, we consider how the influence of companies on AI research can be made measurable on the basis of scientific publishing activities. We compare academic- and company-authored AI publications published in the last decade and use scientometric data from multiple scholarly databases to look for differences across these groups and to disclose the top contributing organizations. While the vast majority of publications is still produced by academia, we find that the citation count an individual publication receives is significantly higher when it is (co-)authored by a company. Furthermore, using a variety of altmetric indicators, we notice that publications with company participation receive considerably more attention online. Finally, we place our analysis results in a broader context and present targeted recommendations to safeguard a harmonious balance between academia and industry in the realm of AI research.},
	keywords     = {scientometrics, industry influence, AI research, bibliometrics, altmetrics},
	pdf          = {publications/AI_Impact_Scientometrics2023.pdf},
tldr = {We measure how companies influence AI research via publishing and show differences in citation impact and online attention between industry and academia.},
plain = {We measure how strongly companies shape AI research through publishing, like tracking who is rowing and who is steering in a shared boat.}
}

@inproceedings{politov2025revisiting,
	title        = {Revisiting Projection-based Data Transfer for Cross-Lingual Named Entity Recognition in Low-Resource Languages},
	author       = {Andrei Politov and Oleh Shkalikov and René Jäkel and Michael Färber},
	year         = 2025,
	booktitle    = {NoDaLiDa/Baltic-HLT},
	url          = {https://arxiv.org/abs/2501.18750},
	abstract     = {Cross-lingual Named Entity Recognition (NER) leverages knowledge transfer between languages to identify and classify named entities, making it particularly useful for low-resource languages. We show that the data-based cross-lingual transfer method is an effective technique for cross-lingual NER and can outperform multilingual language models for low-resource languages. This paper introduces two key enhancements to the annotation projection step in cross-lingual NER for low-resource languages. First, we explore refining word alignments using back-translation to improve accuracy. Second, we present a novel formalized projection approach of matching source entities with extracted target candidates. Through extensive experiments on two datasets spanning 57 languages, we demonstrated that our approach surpasses existing projection-based methods in low-resource settings. These findings highlight the robustness of projection-based data transfer as an alternative to model-based methods for cross-lingual named entity recognition in low-resource languages.},
	keywords     = {cross-lingual NER, annotation projection, low-resource languages, word alignment, data transfer},
	pdf = {publications/XNER_NoDaLiDA2025.pdf},
	location    = {Tallinn, Estonia},
tldr = {We refine projection-based cross-lingual NER transfer and show data-based transfer can outperform multilingual-model baselines in low-resource settings.},
plain = {We revisit cross-language label transfer for name detection, like tracing a drawing onto another sheet, and show how to make the tracing far more accurate.}
}

@inproceedings{DBLP:conf/clef/ZhangRFT13,
	title        = {A Comparative Evaluation of Cross-Lingual Text Annotation Techniques},
	author       = {Lei Zhang and Achim Rettinger and Michael F{\"{a}}rber and Marko Tadic},
	year         = 2013,
	booktitle    = {CLEF},
	publisher    = {Springer},
	volume       = 8138,
	pages        = {124--135},
	doi          = {10.1007/978-3-642-40802-1\_16},
	url          = {https://doi.org/10.1007/978-3-642-40802-1\_16},
	biburl       = {https://dblp.org/rec/conf/clef/ZhangRFT13.bib},
    abstract     = {In this paper, we address the problem of extracting knowledge from multilingual textual documents by annotating text using a cross-lingual knowledge base, namely Wikipedia. Our contribution is twofold. First, we introduce a novel evaluation framework for cross-lingual text annotation techniques that is based on annotating a parallel corpus with respect to a hub language in a cross-lingual knowledge base. Second, we analyze the performance of different cross-lingual text annotation methods within this framework. We conduct an empirical comparison of three approaches: Cross-lingual Named Entity Annotation (CL-NEA), Cross-lingual Wikifier Annotation (CL-WIFI), and Cross-lingual Explicit Semantic Analysis (CL-ESA). The results highlight the strengths and limitations of the investigated approaches and demonstrate the usefulness of the proposed evaluation framework for systematic comparison.},
	keywords     = {cross-lingual annotation, entity linking, Wikipedia, evaluation, multilingual NLP},
	pdf = {publications/TextAnnotation_CLEF2013.pdf},
tldr = {We compare cross-lingual text annotation techniques and introduce an evaluation framework for systematic multilingual knowledge extraction.},
plain = {We compare ways to connect texts with background knowledge across languages, like labeling the same story in different tongues, and we set up a fair “test track” to compare systems.}
}

@inproceedings{DBLP:conf/esws/Farber13,
  title        = {Ontology-Supported Document Ranking for Novelty Search},
  author       = {Michael F{\"{a}}rber},
  year         = 2013,
  booktitle    = {ESWC},
  location     = {Montpellier, France},
  publisher    = {Springer},
  volume       = 7882,
  pages        = {639--644},
  doi          = {10.1007/978-3-642-38288-8\_43},
  url          = {https://doi.org/10.1007/978-3-642-38288-8\_43},
  biburl       = {https://dblp.org/rec/conf/esws/Farber13.bib},
  abstract     = {Within specific domains, users often face the challenge of populating ontologies according to their particular needs. This challenge is especially pronounced in scenarios involving novelty detection and forecasting, where users aim to integrate new information from unstructured text documents into their ontologies for subsequent knowledge-based analysis. In this paper, we propose a semantic document ranking approach that serves as a prerequisite for ontology population. By leveraging the underlying ontology for both query generation and document ranking, the approach introduces semantic structure into the retrieval process and promises improved ranking performance with respect to relevance and novelty compared to non-semantic methods.},
  keywords     = {novelty detection, semantic search, ontology population, document ranking, information retrieval},
  pdf          = {publications/NoveltySearch_ESWC2013.pdf},
tldr = {We rank documents for ontology-supported novelty search to surface novel, relevant information for knowledge-base population and monitoring.},
plain = {We use a knowledge-based compass to rank documents by what is genuinely new, like a treasure hunter looking for fresh finds rather than repeated facts.}
}

@inproceedings{DBLP:conf/dir/FarberR13,
  title        = {A Semantic Wiki for Novelty Search on Documents},
  author       = {Michael F{\"{a}}rber and Achim Rettinger},
  year         = 2013,
  booktitle    = {BIR},
  location     = {Delft, The Netherlands},
  publisher    = {CEUR-WS.org},
  pages        = {60--61},
  url          = {https://ceur-ws.org/Vol-986/paper\_6.pdf},
  biburl       = {https://dblp.org/rec/conf/dir/FarberR13.bib},
  abstract     = {Technology-oriented companies are typically interested in continuously monitoring developments related to their core technologies. However, many organizations, particularly small and medium-sized enterprises, lack efficient and systematic processes for this purpose. Existing efforts are often limited to uncoordinated keyword-based searches over web resources. In this paper, we present a semi-automatic approach that enables the structured and continuous detection of relevant, novel, and domain-specific documents on the Web. Our system is built on a semantic wiki that allows domain experts to (i) store relevant information in a structured knowledge base supporting monitoring and trend mining, and (ii) continuously import newly detected items, such as emerging technologies and their properties, into the knowledge base. Novel items are identified by generating structured queries based on the user’s contextual knowledge and by representing retrieved documents as semantic graphs. This approach facilitates the discovery of novel information in a more effective and semi-automatic manner.},
  keywords     = {semantic wiki, novelty search, technology monitoring, ontology population, semantic retrieval},
  pdf          = {publications/SemanticWiki_BIR2013.pdf},
tldr = {We present a semantic-wiki workflow for novelty search that continuously ingests and structures new web information for ongoing monitoring tasks.},
plain = {We build a semantic wiki as a watchtower that keeps pulling fresh web information into a structured store, so new signals do not get lost in the noise.}
}

@inproceedings{DBLP:conf/esws/Farber0R14,
  title        = {Kuphi - an Investigation Tool for Searching for and via Semantic Relations},
  author       = {Michael F{\"{a}}rber and Lei Zhang and Achim Rettinger},
  year         = 2014,
  booktitle    = {ESWC},
  location     = {Anissaras, Crete, Greece},
  publisher    = {Springer},
  pages        = {349--354},
  doi          = {10.1007/978-3-319-11955-7\_47},
  url          = {https://doi.org/10.1007/978-3-319-11955-7\_47},
  biburl       = {https://dblp.org/rec/conf/esws/Farber0R14.bib},
  abstract     = {In this work, we present Kuphi, a novel process-oriented approach to information retrieval designed for investigating entities and their semantic relations within text documents. Kuphi extends traditional bag-of-words–based search by enabling entity-centric exploration. Starting from a keyword search for a specific entity, users can not only retrieve occurrences of that entity in documents, but also search for related entities by specifying semantic relations of interest. This allows users to indirectly identify textual manifestations of relationships between entities. Through cross-lingual semantic annotation, the query language can differ from the language of the underlying documents. We demonstrate the applicability of Kuphi using DBpedia as the knowledge base and news articles collected from RSS feeds.},
  keywords     = {entity-centric search, semantic relations, information retrieval, cross-lingual annotation, DBpedia},
  pdf          = {publications/Kuphi_ESWC2014.pdf},
tldr = {Kuphi enables entity-centric search and relation-based exploration, letting users investigate documents by following semantic relations, not only keywords.},
plain = {Kuphi helps investigate texts by following who is connected to whom, like pulling a thread from one name to the next until a hidden story appears.}
}

@inproceedings{DBLP:conf/lrec/ZhangFR14,
  title        = {xLiD-Lexica: Cross-lingual Linked Data Lexica},
  author       = {Lei Zhang and Michael F{\"{a}}rber and Achim Rettinger},
  year         = 2014,
  booktitle    = {LREC},
  location     = {Reykjavik, Iceland},
  publisher    = {European Language Resources Association {(ELRA)}},
  pages        = {2101--2105},
  url          = {http://www.lrec-conf.org/proceedings/lrec2014/summaries/248.html},
  biburl       = {https://dblp.org/rec/conf/lrec/ZhangFR14.bib},
  abstract     = {In this paper, we introduce xLiD-Lexica, a set of cross-lingual linked data lexica constructed by exploiting the multilingual content of Wikipedia and resources from the Linked Open Data (LOD) cloud. The lexica provide cross-lingual groundings of linked data resources as RDF, enabling seamless integration into existing LOD datasets. In addition, we offer a SPARQL endpoint over xLiD-Lexica that allows users to conveniently access the lexica using the SPARQL query language. The availability of such multilingual and cross-lingual lexica facilitates cross-lingual information access, for example by enabling the mapping of natural language expressions in different languages to linked data resources. A wide range of natural language processing tasks, including natural language generation, cross-lingual entity linking, text annotation, and question answering, can benefit from xLiD-Lexica.},
  keywords     = {cross-lingual lexica, linked data, multilingual resources, RDF, SPARQL},
  pdf          = {publications/xLiD-Lexica_LREC2014.pdf},
tldr = {We release cross-lingual linked-data lexica that map surface forms to entities across languages, supporting multilingual annotation and information access.},
plain = {We build a cross-language lexicon that links words to the same real-world things, like a multilingual dictionary where “Paris” always means the same place.}
}

@inproceedings{DBLP:conf/semweb/0007FTR14,
  title        = {Exploiting Semantic Annotations for Entity-based Information Retrieval},
  author       = {Lei Zhang and Michael F{\"{a}}rber and Thanh Tran and Achim Rettinger},
  year         = 2014,
  booktitle    = {ISWC},
  location     = {Riva del Garda, Italy},
  volume       = 1272,
  pages        = {429--432},
  url          = {https://ceur-ws.org/Vol-1272/paper\_134.pdf},
  biburl       = {https://dblp.org/rec/conf/semweb/0007FTR14.bib},
  abstract     = {In this paper, we propose a novel approach to entity-based information retrieval that exploits semantic annotations of documents. With the growing availability of structured knowledge bases and semantic annotation techniques, documents and queries can be represented at a semantic level, thereby reducing term ambiguity and bridging language differences between queries and documents. Based on multiple semantic interpretations, users can iteratively refine their queries to better reflect their information needs. By leveraging the semantics of entities and their relations encoded in knowledge bases, we introduce a new ranking scheme designed to more effectively satisfy users’ information needs.},
  keywords     = {entity-based retrieval, semantic annotations, interactive search, knowledge bases, disambiguation},
  pdf          = {publications/EntityIR_ISWC2014.pdf},
tldr = {We show how semantic annotations support entity-based information retrieval and interactive query refinement to reduce ambiguity and improve relevance.},
plain = {We show that tagging text with real entities improves search by meaning, like swapping fuzzy keyword hunting for clear labels you can click and refine.}
}

@inproceedings{DBLP:conf/lrec/0001TJ18,
	title        = {A High-Quality Gold Standard for Citation-based Tasks},
	author       = {Michael F{\"{a}}rber and Alexander Thiemann and Adam Jatowt},
	year         = 2018,
	booktitle    = {LREC},
	location     = {Miyazaki, Japan},
	publisher    = {European Language Resources Association {(ELRA)}},
	url          = {http://www.lrec-conf.org/proceedings/lrec2018/summaries/283.html},
	biburl       = {https://dblp.org/rec/conf/lrec/0001TJ18.bib},
	abstract     = {Analyzing and recommending citations within their specific citation contexts has gained increasing attention due to the rapid growth of scientific literature. Although datasets such as CiteSeerX have been used to evaluate approaches for citation-dependent tasks, they suffer from substantial limitations, stemming from the challenges of information extraction, entity linking, and entity resolution. In this paper, we introduce a new evaluation dataset for citation-dependent tasks based on publications from arXiv.org. The dataset is distinguished by an almost noise-free extraction process and by the fact that all citations are correctly linked to their referenced publications. In addition to providing sentence-level textual content, cited publications are annotated directly within the text using global identifiers and, where possible, are further linked to the DBLP Computer Science Bibliography. The resulting dataset comprises over 15 million sentences and is freely available for research purposes. It supports the training and evaluation of a wide range of citation-based tasks, including citation recommendation, citation function and importance classification, and citation-based document summarization.},
	keywords     = {citation contexts, gold standard dataset, citation recommendation, scholarly NLP, arXiv},
	pdf          = {publications/CitationGold_LREC2018.pdf},
tldr = {We create a high-quality arXiv-based gold standard with correctly linked citations, enabling reliable evaluation of citation-centric NLP tasks.},
plain = {We build a carefully checked reference set where citations in arXiv full texts are linked correctly.}
}

@inproceedings{DBLP:conf/edbt/Cossu0L18,
	title        = {PRoST: Distributed Execution of {SPARQL} Queries Using Mixed Partitioning Strategies},
	author       = {Matteo Cossu and Michael F{\"{a}}rber and Georg Lausen},
	year         = 2018,
	booktitle    = {EDBT},
	location     = {Vienna, Austria},
	pages        = {469--472},
	doi          = {10.5441/002/EDBT.2018.49},
	url          = {https://doi.org/10.5441/002/edbt.2018.49},
	biburl       = {https://dblp.org/rec/conf/edbt/Cossu0L18.bib},
    abstract     = {The rapidly increasing size of RDF graphs in recent years has created a strong need for distributed storage and parallel query processing strategies. To enable efficient query processing on computer clusters, a wide range of approaches has been proposed, including systems built on top of Hadoop HDFS using technologies such as Apache Accumulo or Apache Spark. In this paper, we introduce a new RDF store called PRoST (Partitioned RDF on Spark Tables) that is based on Apache Spark. PRoST employs a novel storage strategy that combines vertical partitioning with property tables, two established models for RDF data storage. We show that PRoST outperforms state-of-the-art RDF stores in terms of query execution time across a broad spectrum of query types, while requiring no extensive precomputation phase.},
	keywords     = {SPARQL, distributed RDF store, Apache Spark, query processing, partitioning},
	pdf          = {publications/PRoST_EDBT2018.pdf},
tldr = {PRoST is a Spark-based RDF store that speeds up SPARQL by combining vertical partitioning and property tables for scalable graph querying.},
plain = {PRoST speeds up queries on linked data by changing the “warehouse layout”, so answers can be found faster on big computing clusters.}
}

@inproceedings{DBLP:conf/ecir/0001TJ18a,
	title        = {CITEWERTs: {A} System Combining Cite-Worthiness with Citation Recommendation},
	author       = {Michael F{\"{a}}rber and Alexander Thiemann and Adam Jatowt},
	year         = 2018,
	booktitle    = {ECIR},
	location     = {Grenoble, France},
	publisher    = {Springer},
	pages        = {815--819},
	doi          = {10.1007/978-3-319-76941-7\_82},
	url          = {https://doi.org/10.1007/978-3-319-76941-7\_82},
	biburl       = {https://dblp.org/rec/conf/ecir/0001TJ18a.bib},
    abstract     = {The vast and continuously growing number of scientific publications has created a need for automatically recommending citations for specific text segments in scholarly documents. Despite this demand, only a limited number of citation-based recommender system demonstrations have been proposed so far. Existing solutions often either ignore the raw textual context, recommend citations only for predefined citation contexts, or operate at the document level. In contrast, we propose a novel two-step architecture for citation recommendation. First, given an input text, the system determines for each potential citation context—typically at the sentence level—whether it is actually citation-worthy. If so, the system then recommends suitable citations for that context. In our demonstration, we illustrate how this architecture guides users directly to sentences that require citations and presents relevant recommendations for individual sentences, thereby reducing the effort needed to review large numbers of sentences and suggested references.},
    keywords     = {cite-worthiness, citation recommendation, scholarly writing, sentence-level recommendation, information retrieval},
	pdf          = {publications/CITEWERTs_ECIR2018.pdf},
tldr = {CITEWERTs combines cite-worthiness detection with citation recommendation to guide writers to sentences that need citations and suggest references.},
plain = {CITEWERT first checks whether a sentence really needs a citation, like a spell-checker for missing references, and then suggests suitable papers.}
}

@inproceedings{DBLP:conf/ecir/0001TJ18,
	title        = {To Cite, or Not to Cite? Detecting Citation Contexts in Text},
	author       = {Michael F{\"{a}}rber and Alexander Thiemann and Adam Jatowt},
	year         = 2018,
	booktitle    = {ECIR},
	location     = {Grenoble, France},
	publisher    = {Springer},
	pages        = {598--603},
	doi          = {10.1007/978-3-319-76941-7\_50},
	url          = {https://doi.org/10.1007/978-3-319-76941-7\_50},
	biburl       = {https://dblp.org/rec/conf/ecir/0001TJ18.bib},
    abstract     = {Recommending citations for scientific texts and other document types, such as news articles, has recently received considerable attention. Existing citation recommendation approaches, however, typically do not explicitly address whether a given text segment, for example a sentence, actually deserves a citation. Introducing a prior step that determines the cite-worthiness of potential citation contexts is advantageous, as it both reduces the number of computationally expensive recommendation operations and better reflects human citation behavior by avoiding excessive or insufficient recommendations. In this paper, we propose a convolutional recurrent neural network–based approach for classifying potential citation contexts with respect to their cite-worthiness. Our experimental results show that the proposed method significantly outperforms a baseline approach and reduces the number of citation recommendations to approximately one tenth.},
    keywords     = {cite-worthiness, citation context detection, neural networks, scholarly text mining, citation recommendation},
	pdf          = {publications/CitationContext_ECIR2018.pdf},
tldr = {We detect which sentences truly require citations, reducing unnecessary recommendations and better matching real scientific writing behavior.},
plain = {We teach a system to spot which sentences should cite sources.}
}

@inproceedings{DBLP:conf/semweb/Farber19,
	title        = {The Microsoft Academic Knowledge Graph: {A} Linked Data Source with 8 Billion Triples of Scholarly Data},
	author       = {Michael F{\"{a}}rber},
	year         = 2019,
	booktitle    = {ISWC},
	location     = {Auckland, New Zealand},
	publisher    = {Springer},
	volume       = 11779,
	pages        = {113--129},
	doi          = {10.1007/978-3-030-30796-7\_8},
	url          = {https://doi.org/10.1007/978-3-030-30796-7\_8},
	biburl       = {https://dblp.org/rec/conf/semweb/Farber19.bib},
    abstract     = {In this paper, we present the Microsoft Academic Knowledge Graph (MAKG), a large-scale RDF dataset comprising over eight billion triples that describe scientific publications and related entities, including authors, institutions, journals, and fields of study. The dataset is released under the Open Data Commons Attribution License (ODC-By). By providing the data both as RDF dump files and as a data source within the Linked Open Data cloud—featuring resolvable URIs and links to external data sources—we make a substantial body of scholarly data available on the Web of Data. In addition, we provide entity embeddings for all 210 million publications represented in the graph. The MAKG supports a wide range of use cases, particularly in the domain of digital libraries, such as (1) entity-centric exploration of publications, researchers, and affiliations; (2) data integration tasks using RDF as a common data model and interlinking with other datasets; and (3) large-scale data analysis and knowledge discovery over scholarly data.},
    keywords     = {scholarly knowledge graph, linked data, RDF dataset, Microsoft Academic Graph, entity embeddings},
	pdf          = {publications/MAKG_ISWC2019.pdf},
tldr = {We release the Microsoft Academic Knowledge Graph in RDF with billions of triples and embeddings, enabling large-scale scholarly integration and analytics.},
plain = {We publish a huge linked dataset of scholarly metadata, like turning a library card catalog into a connected web map that machines can navigate.}
}

@inproceedings{DBLP:conf/clef/0001QA19,
	title        = {Identifying Twitter Bots Using a Convolutional Neural Network},
	author       = {Michael F{\"{a}}rber and Agon Qurdina and Lule Ahmedi},
	year         = 2019,
	booktitle    = {CLEF},
	location     = {Lugano, Switzerland},
	publisher    = {CEUR-WS.org},
	volume       = 2380,
	url          = {https://ceur-ws.org/Vol-2380/paper\_227.pdf},
	biburl       = {https://dblp.org/rec/conf/clef/0001QA19.bib},
    abstract     = {In this paper, we present an approach for identifying Twitter bots based on the textual content of their tweets using a convolutional neural network. We experiment with various word embedding methods, including both pretrained embeddings and embeddings learned directly from the training data, as well as with different convolutional neural network architectures, and systematically compare their performance. When evaluated on the official test set of the CLEF~2019 Bots Profiling Subtask for the English language, our best-performing model achieves an accuracy of 90.34\%. These results indicate that convolutional neural networks constitute a promising machine learning technique for Twitter bot detection.},
    keywords     = {bot detection, Twitter, text classification, convolutional neural networks, social media},
	pdf          = {publications/TwitterBots_CLEF2019.pdf},
tldr = {We detect Twitter bots from tweet text with CNNs and achieve strong accuracy on a shared evaluation task for automated bot profiling.},
plain = {We detect Twitter bots from their writing style, like using a metal detector to separate automated noise from real people.}
}

@inproceedings{DBLP:conf/ercimdl/FarberS19,
	title        = {Determining How Citations Are Used in Citation Contexts},
	author       = {Michael F{\"{a}}rber and Ashwath Sampath},
	year         = 2019,
	booktitle    = {TPDL},
	location     = {Oslo, Norway},
	publisher    = {Springer},
	volume       = 11799,
	pages        = {380--383},
	doi          = {10.1007/978-3-030-30760-8\_38},
	url          = {https://doi.org/10.1007/978-3-030-30760-8\_38},
	biburl       = {https://dblp.org/rec/conf/ercimdl/FarberS19.bib},
    abstract     = {Citations have previously been classified based on their textual contexts with respect to criteria such as worthiness, function, polarity, and importance. To the best of our knowledge, however, citations have not yet been automatically classified according to their grammatical role, that is, whether a citation (1) is grammatically integrated into the sentence, (2) is placed directly after an occurrence of author names, (3) supports a concept, (4) supports a claim, or (5) is inappropriate due to incomplete or noisy context. We argue that identifying such classes of citation contexts is beneficial for various tasks, including improved citation recommendation and scientific impact quantification. In this paper, we introduce this classification scheme and propose a machine-learning-based approach for automatically assigning citation contexts to these classes. Our evaluation shows that classification performance differs substantially across the various citation types.},
    keywords     = {citation contexts, citation classification, citation function, scholarly NLP, citation recommendation},
	pdf          = {publications/CitationUsage_TPDL2019.pdf},
tldr = {We define fine-grained categories of citation usage and train models to classify them, improving citation analysis and recommendation.},
plain = {We label how citations are used inside sentences, like tagging whether a reference backs a claim or just explains a concept.}
}

@inproceedings{DBLP:conf/sigir/NishiokaF19,
	title        = {Evaluating the Availability of Open Citation Data},
	author       = {Chifumi Nishioka and Michael F{\"{a}}rber},
	year         = 2019,
	booktitle    = {BIRNDL@SIGIR},
	location     = {Paris, France},
	volume       = 2414,
	pages        = {123--129},
	url          = {https://ceur-ws.org/Vol-2414/paper13.pdf},
	editor       = {Muthu Kumar Chandrasekaran and Philipp Mayr},
    abstract     = {Citation data of scientific publications is essential for a variety of purposes, including research evaluation and the construction of digital library collections. In this paper, we analyze the extent to which citation data of scholarly publications is openly available. Our analysis is based on the intersection of Crossref metadata and an Unpaywall snapshot as the publication dataset, and the COCI dataset as the source of open citation data. We show that citation data is openly available for 24.2\% of publications, while it is closed for 16.6\%. Furthermore, we find that the proportion of publications with open citation data has increased over time. Our results indicate that publishing a paper as open access does not influence whether its citation data is openly available. However, publications appearing in journals listed in the Directory of Open Access Journals (DOAJ) tend to have a higher share of openly available citation data compared to publications from other journals.},
    keywords     = {open citations, citation data, bibliometrics, open access, Crossref},
	pdf          = {publications/OpenCitations_SIGIR2019.pdf},
tldr = {We quantify how much citation data is openly available and how openness evolves over time, informing open-science policy and research evaluation.},
plain = {We take a census of open citation data and measure how much of the scientific reference network is visible to the public and how it changes over time.}
}

@inproceedings{DBLP:conf/sigmod/AyalaKAC0PSTL19,
	title        = {Relational schemata for distributed {SPARQL} query processing},
	author       = {Victor Anthony Arrascue Ayala and Polina Koleva and Anas Alzogbi and Matteo Cossu and Michael F{\"{a}}rber and Patrick Philipp and Guilherme Schievelbein and Io Taxidou and Georg Lausen},
	year         = 2019,
	booktitle    = {SBD@SIGMOD},
	location     = {Amsterdam, The Netherlands},
	pages        = {3:1--3:6},
	doi          = {10.1145/3323878.3325804},
	url          = {https://doi.org/10.1145/3323878.3325804},
    abstract     = {To leverage mature database technologies, RDF stores are often built on top of relational databases, with SPARQL queries translated into SQL. Scalability can be achieved by executing query processing over large RDF datasets in a distributed manner using shared-nothing computer clusters. In this paper, we investigate the impact of relational schema design when SPARQL queries are mapped to Apache Spark SQL. We consider several schema designs, including a single triple table, predicate-partitioned tables, a single wide table covering all properties, and a set of tables derived from an application model specification, referred to as a domain-dependent schema. For each approach, the corresponding tables are stored in the distributed file system HDFS using the columnar storage format Parquet. Experiments conducted with standard benchmarks show that the single wide property table approach, despite its simplicity, outperforms the other designs. Additional experiments further demonstrate that this approach remains advantageous even when repartitioning by key (RDF subject) is applied prior to query execution.},
    keywords     = {SPARQL, distributed query processing, relational schema, Apache Spark, RDF storage},
	pdf          = {publications/SPARQL_Schemata_SIGMOD2019.pdf},
tldr = {We compare relational schema designs for distributed SPARQL on Spark and show how schema choice impacts performance on standard RDF benchmarks.},
plain = {We compare different storage “blueprints” for linked data on big clusters, like testing how shelf layouts change how fast you find the right book.}
}

@inproceedings{DBLP:conf/jcdl/0001NJ19,
	title        = {ScholarSight: Visualizing Temporal Trends of Scientific Concepts},
	author       = {Michael F{\"{a}}rber and Chifumi Nishioka and Adam Jatowt},
	year         = 2019,
	booktitle    = {JCDL},
	location     = {Champaign, IL, USA},
	publisher    = {{IEEE}},
	pages        = {438--439},
	doi          = {10.1109/JCDL.2019.00108},
	url          = {https://doi.org/10.1109/JCDL.2019.00108},
    abstract     = {In this paper, we present a system for exploring temporal trends of scientific concepts. The concepts are extracted by identifying noun phrases and entities from all computer science publications available on arXiv.org. The system enables users to examine time series for a large number of concepts and to identify both positively and negatively trending concepts over time. By applying clustering techniques and visualizations for cluster analysis, the system further reveals groups of concepts that exhibit similar usage patterns across different periods. This system supports exploratory analyses for researchers across scientific disciplines and provides a useful tool for scholars in bibliometrics and scientometrics to study the evolution of scientific concepts.},
    keywords     = {trend analysis, scientific concepts, visualization, time series, scientometrics},
	pdf          = {publications/ScholarSight_JCDL2019.pdf},
tldr = {ScholarSight visualizes temporal trends of scientific concepts extracted from papers, helping researchers explore emerging and fading topics.},
plain = {ScholarSight works like a trend telescope for science, showing which ideas rise or fade so researchers can see what is heating up and what is cooling down.}
}

@inproceedings{DBLP:conf/semeval/FarberQA19,
	title        = {Team Peter Brinkmann at SemEval-2019 Task 4: Detecting Biased News Articles Using Convolutional Neural Networks},
	author       = {Michael F{\"{a}}rber and Agon Qurdina and Lule Ahmedi},
	year         = 2019,
	booktitle    = {SemEval@NAACL-HLT 2019},
	location     = {Minneapolis, MN, USA},
	pages        = {1032--1036},
	doi          = {10.18653/V1/S19-2180},
	url          = {https://doi.org/10.18653/v1/s19-2180},
    abstract     = {In this paper, we present an approach for classifying news articles as biased, that is, hyperpartisan, or unbiased using a convolutional neural network. We experiment with various word embedding methods, including both pretrained embeddings and embeddings learned directly from the training data, as well as with different convolutional neural network architectures, and compare their performance. When evaluated on the official test set of SemEval~2019 Task~4, our best-performing model achieves relatively low precision and accuracy, but attains the highest recall among all 42 participating teams.},
    keywords     = {bias detection, hyperpartisan news, text classification, convolutional neural networks, media analysis},
	pdf          = {publications/BiasedNews_SemEval2019.pdf},
tldr = {We detect hyperpartisan biased news with neural text models and analyze trade-offs in precision and recall on a large shared-task benchmark.},
plain = {We build systems that flag extremely one-sided news, like an early warning label for strongly biased reporting, and we study where these systems fail.}
}

@inproceedings{DBLP:conf/ecir/SaierF19,
	title        = {Bibliometric-Enhanced arXiv: {A} Data Set for Paper-Based and Citation-Based Tasks},
	author       = {Tarek Saier and Michael F{\"{a}}rber},
	year         = 2019,
	booktitle    = {BIR@ECIR},
	location     = {Cologne, Germany},
	volume       = 2345,
	pages        = {14--26},
	url          = {https://ceur-ws.org/Vol-2345/paper2.pdf},
    abstract     = {In recent years, a variety of research paper–based tasks, such as paper recommendation, as well as citation-based tasks, including citation recommendation and citation context-based document summarization, have been proposed. The evaluation of methods for these tasks and their applicability in real-world settings strongly depend on the underlying datasets. However, existing datasets suffer from several limitations. In this paper, we introduce a new dataset derived from all publications across all scientific disciplines available on arXiv.org. In addition to providing the full plain text of papers, in-text citations are annotated using global identifiers, and cited publications are linked to the Microsoft Academic Graph wherever possible. The resulting dataset comprises over one million documents and 29.2 million citation contexts. Freely available for research purposes, this dataset supports more reliable evaluation of research paper–based and citation context–based approaches and also serves as a foundation for novel methods to analyze scientific publications.},
	keywords     = {scholarly dataset, citation contexts, in-text citations, arXiv, bibliometrics},
	pdf          = {publications/unarXive_ECIR2019.pdf},
tldr = {We provide a bibliometric-enhanced arXiv dataset for paper- and citation-based tasks.},
plain = {We add citation links and metadata to arXiv papers, like attaching extra labels to millions of documents so recommendation tools can be tested fairly.}
}

@inproceedings{DBLP:conf/ecir/FarberJ19,
	title        = {Finding Temporal Trends of Scientific Concepts},
	author       = {Michael F{\"{a}}rber and Adam Jatowt},
	year         = 2019,
	booktitle    = {BIR@ECIR},
	location     = {Cologne, Germany},
	volume       = 2345,
	pages        = {132--139},
	url          = {https://ceur-ws.org/Vol-2345/paper12.pdf},
    abstract     = {Science evolves rapidly, and prior work has primarily investigated the evolution of coarse-grained research topics. To the best of our knowledge, however, no large-scale analysis of temporal trends of fine-grained scientific concepts based on full-text publications has been conducted. In this paper, we extract noun phrases as fine-grained concepts from the full texts of all computer science papers available on arXiv.org. We then identify positive and negative temporal trends using simple linear regression, the Mann--Kendall test, and the Theil--Sen estimator. Our experimental results show that the Mann--Kendall test yields particularly meaningful trend detection outcomes, whereas the Theil--Sen estimator and simple linear regression often identify non-scientific concepts as trending. The presented findings are relevant for both general researchers and scholars in bibliometrics and scientometrics who are interested in analyzing the evolution of scientific concepts.},
	keywords     = {trend detection, scientific concepts, time series analysis, bibliometrics, arXiv},
	pdf          = {publications/TrendsConcepts_BIR2019.pdf},
tldr = {We track fine-grained scientific concept trends in arXiv full texts using statistical trend tests, helping understand how research topics evolve.},
plain = {We track fine-grained scientific ideas over time, like a seismograph for concepts that shows which ones steadily grow and which ones fade.}
}

@inproceedings{DBLP:conf/ecir/FarberSJ19,
	title	      = {PaperHunter: A System for Exploring Papers and Citation Contexts},
	author       = {Michael F{\"{a}}rber and Ashwath Sampath and Adam Jatowt},
	year         = 2019,
	booktitle    = {ECIR},
	location     = {Cologne, Germany},
	publisher    = {Springer},
	volume       = 11438,
	pages        = {246--250},
	doi          = {10.1007/978-3-030-15719-7_33},
	url          = {https://doi.org/10.1007/978-3-030-15719-7_33},
    abstract     = {In this paper, we present a system that enables researchers to search for scientific papers and in-text citations in a novel manner. In particular, the system allows users to retrieve the textual contexts in which publications are cited, referred to as citation contexts, by specifying either the cited paper’s title or the cited author’s name. To support qualitative assessment of citations, the system provides information on citation polarity, indicating whether a cited work is discussed in a positive, neutral, or negative manner. The system is built on the full corpus of computer science papers available on arXiv.org and can be used both by computer science researchers to reflect on how their work is perceived within the scientific community and by scholars conducting citation analysis research.},
	keywords     = {citation contexts, scientific search, digital libraries, citation polarity, exploratory search},
	pdf          = {publications/PaperHunter_Demo_ECIR2019.pdf},
tldr = {PaperHunter lets users search citation contexts and polarity, enabling qualitative exploration of how papers are cited and discussed.},
plain = {PaperHunter lets you search the exact sentences where a paper is cited, like finding every time a name is mentioned and reading the surrounding context.}
}

@inproceedings{DBLP:conf/jcdl/000120,
	title        = {Analyzing the GitHub Repositories of Research Papers},
	author       = {Michael F{\"{a}}rber},
	year         = 2020,
	booktitle    = {JCDL},
	location     = {Virtual Event, China},
	publisher    = {{ACM}},
	pages        = {491--492},
	doi          = {10.1145/3383583.3398578},
	url          = {https://doi.org/10.1145/3383583.3398578},
	biburl       = {https://dblp.org/rec/conf/jcdl/000120.bib},
    abstract      = {Linking to code repositories, such as those hosted on GitHub, has become increasingly common in scientific publications, particularly in computer science. However, the actual quality, maintenance, and usage of these repositories have so far remained largely unexplored. In this paper, we present the first comprehensive analysis of all GitHub code repositories referenced in scientific papers, using the Microsoft Academic Graph as the underlying data source. We analyze both the repositories and their associated publications across multiple dimensions. Our results show that the numbers of stars and forks across repositories follow a power-law distribution. In most cases, only a single author of a paper actively contributes to the corresponding repository. Repository documentation is typically concise, consisting of only a few sentences, and the source code is predominantly written in Python. The publications linking to code repositories, as well as their authors, are primarily associated with the artificial intelligence research community.},
	keywords     = {research code, GitHub, software repositories, scientometrics, open science},
    pdf          = {publications/GitHub_JCDL2020.pdf},
tldr = {We analyze GitHub repositories linked from research papers to study maintenance and documentation patterns, providing signals for research reproducibility.},
plain = {We analyze the GitHub code behind research papers, like checking the health of the software that scientific results depend on in practice.}
}

@inproceedings{DBLP:conf/jcdl/0001S20,
	title        = {HybridCite: {A} Hybrid Model for Context-Aware Citation Recommendation},
	author       = {Michael F{\"{a}}rber and Ashwath Sampath},
	year         = 2020,
	booktitle    = {JCDL},
	location     = {Virtual Event, China},
	publisher    = {{ACM}},
	pages        = {117--126},
	doi          = {10.1145/3383583.3398534},
	url          = {https://doi.org/10.1145/3383583.3398534},
	biburl       = {https://dblp.org/rec/conf/jcdl/0001S20.bib},
    abstract     = {Citation recommendation systems seek to suggest relevant references either for an entire paper or for a specific text passage, referred to as a citation context. Recommending citations at the level of citation contexts, known as local citation recommendation, is the focus of this paper. We first develop citation recommendation approaches based on embeddings, topic modeling, and information retrieval techniques. To the best of our knowledge, we then combine the best-performing methods for the first time into a semi-genetic hybrid citation recommender system. We evaluate both the individual approaches and the hybrid model in offline experiments using several datasets, including the Microsoft Academic Graph (MAG) as well as MAG combined with arXiv and ACL. In addition, we conduct an online user study to assess the proposed methods. Our results demonstrate that a hybrid model integrating embedding-based and information retrieval–based components substantially outperforms its individual components as well as other baseline approaches.},
	keywords     = {citation recommendation, context-aware recommendation, hybrid models, information retrieval, embeddings},
    pdf          = {publications/HybridCite_JCDL2020.pdf},
tldr = {HybridCite combines embedding and IR signals to improve context-aware citation recommendation beyond single-method baselines.},
plain = {HybridCite recommends citations by combining several signals, like using both a compass and a map so suggestions fit the writing context better.}
}

@inproceedings{DBLP:conf/icwe/0001SB20,
	title        = {Who's Behind That Website? Classifying Websites by the Degree of Commercial Intent},
	author       = {Michael F{\"{a}}rber and Benjamin Scheer and Frederic Bartscherer},
	year         = 2020,
	booktitle    = {ICWE},
	location     = {Helsinki, Finland},
	publisher    = {Springer},
	volume       = 12128,
	pages        = {130--145},
	doi          = {10.1007/978-3-030-50578-3\_10},
	url          = {https://doi.org/10.1007/978-3-030-50578-3\_10},
	biburl       = {https://dblp.org/rec/conf/icwe/0001SB20.bib},
    abstract     = {Web hosting companies aim to provide customized customer services and therefore seek to identify the commercial intent of websites. Determining whether a website is operated by an individual, a company, a non-profit organization, or a public institution is a challenging classification task, as website content is often sparse or limited. In this paper, we present a novel approach for identifying the commercial intent of websites using a combination of supervised and unsupervised machine learning techniques. Based on a large real-world dataset, we evaluate our model in terms of effectiveness and efficiency and find that a multilayer perceptron achieves the best performance.},
	keywords     = {web classification, commercial intent, website profiling, machine learning, content analysis},
    pdf          = {publications/Website-Classification_ICWE2020.pdf},
tldr = {We classify websites by commercial intent with ML, helping distinguish individuals, companies, non-profits, and public institutions from sparse web content.},
plain = {We sort websites by whether they are commercial or not, like a quick “smell test” that works even when there is little text.}
}

@inproceedings{DBLP:conf/lrec/LimJFY20,
	title        = {Annotating and Analyzing Biased Sentences in News Articles using Crowdsourcing},
	author       = {Sora Lim and Adam Jatowt and Michael F{\"{a}}rber and Masatoshi Yoshikawa},
	year         = 2020,
	booktitle    = {LREC},
	location     = {Marseille, France},
	publisher    = {European Language Resources Association},
	pages        = {1478--1484},
	url          = {https://aclanthology.org/2020.lrec-1.184/},
	biburl       = {https://dblp.org/rec/conf/lrec/LimJFY20.bib},
    abstract     = {The spread and consumption of biased news has become a significant societal concern. Researchers from various fields, including social sciences and media studies, have proposed methods to address media bias, often leveraging techniques from natural language processing and machine learning to automatically identify biased content. However, progress in this area has been hindered by the lack of publicly available datasets, particularly those providing fine-grained bias annotations at the sentence level. In this paper, we introduce a novel news bias dataset designed to support the development and evaluation of methods for detecting subtle bias in news articles and for analyzing the characteristics of biased sentences. The dataset comprises 966 sentences from 46 English-language news articles covering four distinct events and includes sentence-level bias annotations. To ensure scalability, the annotations were obtained via crowdsourcing. Our dataset enables detailed analyses of news bias and serves as a benchmark for developing and evaluating bias detection methods, as well as a resource for related research areas such as fake news detection.},
	keywords     = {media bias, dataset, crowdsourcing, sentence-level annotations, news analysis},
    pdf          = {publications/NewsBias_LREC2020.pdf},
tldr = {We create and analyze a crowdsourced dataset of biased sentence annotations, enabling fine-grained study of subtle news bias.},
plain = {We crowdsource labels of subtle bias in news, like using many small magnifying glasses to spot slanted wording sentence by sentence.}
}

@inproceedings{NoulletMF20,
	title        = {{KORE} 50^{DYWC}: An Evaluation Data Set for Entity Linking Based on DBpedia, YAGO, Wikidata, and Crunchbase},
	author       = {Kristian Noullet and Rico Mix and Michael F{\"{a}}rber},
	year         = 2020,
	booktitle    = {LREC},
	location     = {Marseille, France},
	publisher    = {European Language Resources Association},
	pages        = {2389--2395},
	url          = {https://aclanthology.org/2020.lrec-1.291/},
    abstract     = {Named entity recognition and disambiguation (NERD) constitutes a major research area in natural language processing. A common approach to addressing this task relies on Semantic Web technologies and structured data formats, which facilitate information extraction and enable the construction of knowledge graphs. To properly evaluate NERD systems, high-quality gold standard datasets are required. While numerous evaluation datasets exist, they are predominantly based on Wikipedia or DBpedia. In this work, we extend the widely used gold standard dataset KORE 50 to support NERD evaluation not only for DBpedia, but also for YAGO, Wikidata, and Crunchbase. The resulting dataset, KORE~50$^{DYWC}$, enables a substantially broader evaluation landscape. In particular, it allows for the assessment of the knowledge-graph agnosticity of NERD systems across multiple knowledge graphs, which, to the best of our knowledge, has not previously been possible at this scale.},
	keywords     = {entity linking, NERD, evaluation dataset, DBpedia, Wikidata},
    pdf          = {publications/KORE50-DYWC_LREC2020.pdf},
tldr = {We extend the KORE 50 entity-linking benchmark to multiple knowledge graphs, enabling broader evaluation across DBpedia, Wikidata, YAGO, and Crunchbase.},
plain = {We take a well-known test set for linking names to real-world entities and expand it to several different knowledge bases, like checking whether a navigation system still works when you switch from one city map to another.}
}

@inproceedings{DBLP:conf/ecir/WangJ0Y20,
	title        = {Answering Event-Related Questions over Long-Term News Article Archives},
	author       = {Jiexin Wang and Adam Jatowt and Michael F{\"{a}}rber and Masatoshi Yoshikawa},
	year         = 2020,
	booktitle    = {ECIR},
	location     = {Lisbon, Portugal},
	publisher    = {Springer},
	volume       = 12035,
	pages        = {774--789},
	doi          = {10.1007/978-3-030-45439-5\_51},
	url          = {https://doi.org/10.1007/978-3-030-45439-5\_51},
	biburl       = {https://dblp.org/rec/conf/ecir/WangJ0Y20.bib},
    abstract     = {Long-term news article archives constitute valuable resources for understanding past events, as they provide detailed information about occurrences at specific points in time. To better exploit such heritage collections, this work addresses the task of large-scale question answering over long-term news archives. Questions posed to these archives are often event-related and typically exhibit strong temporal characteristics. They can be broadly categorized into two types: those containing explicit temporal expressions and those that are only implicitly associated with particular time periods. We focus on the latter category, as such questions are more challenging to answer, and propose a retriever–reader model augmented with an additional module that reranks articles by leveraging temporal information from multiple perspectives. Experimental results on a carefully constructed test set demonstrate that our approach outperforms existing question answering systems, primarily due to the enhanced document retrieval enabled by the temporal reranking module.},
	keywords     = {question answering, news archives, temporal information retrieval, event-centric QA, reranking},
    pdf          = {publications/QA_ECIR2020.pdf},
tldr = {We answer event-related questions over long-term news archives using temporal reranking, improving retrieval when questions have implicit time scopes.},
plain = {We answer event questions in huge news archives using time clues, like flipping to the right chapter of history even when the question forgets to name the year.}
}

@inproceedings{DBLP:conf/ecir/Saier020,
	title        = {Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation},
	author       = {Tarek Saier and Michael F{\"{a}}rber},
	year         = 2020,
	booktitle    = {ECIR},
	location     = {Lisbon, Portugal},
	publisher    = {Springer},
	volume       = 12035,
	pages        = {220--233},
	doi          = {10.1007/978-3-030-45439-5\_15},
	url          = {https://doi.org/10.1007/978-3-030-45439-5\_15},
	biburl       = {https://dblp.org/rec/conf/ecir/Saier020.bib},
    abstract     = {The rapid pace at which new research is being published makes it increasingly infeasible for scholars to read and assess all potentially relevant work. To address this challenge, automated processing of scientific publications—such as semantic modeling to support digital handling and the development of information filtering systems—has become an active research area. In this paper, we investigate the benefits of semantically modeling citation contexts for citation recommendation. To this end, we develop semantic representations of citation contexts based on entities and claim structures. We evaluate the effectiveness and conceptual validity of the proposed models through a large-scale offline evaluation on multiple datasets and complement this analysis with a user study. Our results show that the semantic models outperform a non-semantic baseline and effectively capture the types of information they are designed to represent.},
	keywords     = {citation contexts, citation recommendation, semantic modeling, scholarly NLP, entity-based representations},
    pdf          = {publications/SemCiteRec_ECIR2020.pdf},
tldr = {We model citation contexts using entities and claim structure and show improved context-aware citation recommendation at scale.},
plain = {We turn citation contexts into structured signals, like extracting the ingredients and purpose of a reference so models understand more than nearby words.}
}

@inproceedings{DBLP:conf/birws/FarberKS20,
	title        = {Neural Citation Recommendation: {A} Reproducibility Study},
	author       = {Michael F{\"{a}}rber and Timo Klein and Joan Sigloch},
	year         = 2020,
	booktitle    = {BIR@ECIR},
	location     = {Lisbon, Portugal},
	publisher    = {CEUR-WS.org},
	volume       = 2591,
	pages        = {66--74},
	url          = {https://ceur-ws.org/Vol-2591/paper-07.pdf},
	biburl       = {https://dblp.org/rec/conf/birws/FarberKS20.bib},
    abstract     = {Context-aware citation recommendation aims to reduce the manual effort of searching for relevant references by automatically suggesting suitable papers as citations for a given input text. In this paper, we investigate the reproducibility of a state-of-the-art approach to context-aware citation recommendation, namely the Neural Citation Network (NCN) proposed by Ebesu and Fang. We reimplement the network and evaluate it on both RefSeer, the dataset used in the original work, and arXiv CS as an additional dataset. Furthermore, we analyze the impact of different neural network hyperparameters on the performance of the NCN and demonstrate how they can be tuned to improve results. Through this study, we contribute to increased transparency in citation recommendation research and support the development of more effective neural network–based models and evaluation practices.},
	keywords     = {citation recommendation, reproducibility, neural networks, context-aware recommendation, evaluation},
    pdf          = {publications/CiteRec_Repro_BIR2020.pdf},
tldr = {We reimplement a neural citation recommender and analyze hyperparameter effects, improving reproducibility and providing practical tuning guidance.},
plain = {We rebuild a well-known citation recommender from scratch, like re-running a recipe to check whether others can truly reproduce the same result.}
}

@inproceedings{DBLP:conf/cikm/0001L21,
	title        = {Recommending Datasets for Scientific Problem Descriptions},
	author       = {Michael F{\"{a}}rber and Ann{-}Kathrin Leisinger},
	year         = 2021,
	booktitle    = {CIKM},
	location     = {Virtual Event, Queensland, Australia},
	pages        = {3014--3018},
	doi          = {10.1145/3459637.3482166},
	url          = {https://doi.org/10.1145/3459637.3482166},
    abstract     = {The rapidly growing number of available datasets makes it increasingly difficult for researchers and practitioners to identify the most relevant datasets for a given research problem, a challenge that existing keyword-based dataset search engines address only imperfectly. In this paper, we propose a system that recommends suitable datasets directly from research problem descriptions, formulating the task as a domain-specific text classification problem. In a comprehensive offline evaluation using state-of-the-art models and large-scale data comprising 88,000 paper abstracts and 265,000 citation contexts as problem descriptions, our approach achieves an F1 score of 0.75. Additionally, a user study in real-world settings shows that users are satisfied in 88% of all test cases. These results indicate promising future directions for dataset recommendation systems.},
	keywords     = {dataset recommendation, scientific text classification, data discovery, scholarly NLP, information retrieval},
    pdf          = {publications/DataRec_CIKM2021.pdf},
tldr = {We propose dataset recommendation from natural-language scientific problem descriptions, cutting dataset search time and improving dataset reuse at scale.},
plain = {You describe your research problem in plain text and we suggest suitable datasets, like a matchmaking service that connects questions with the data that can answer them.}
}

@inproceedings{DBLP:conf/recsys/0001L21,
	title        = {DataHunter: {A} System for Finding Datasets Based on Scientific Problem Descriptions},
	author       = {Michael F{\"{a}}rber and Ann{-}Kathrin Leisinger},
	year         = 2021,
	booktitle    = {RecSys},
	location     = {Amsterdam, The Netherlands},
	publisher    = {{ACM}},
	pages        = {749--752},
	doi          = {10.1145/3460231.3478882},
	url          = {https://doi.org/10.1145/3460231.3478882},
    abstract     = {The steadily increasing number of datasets makes it difficult for researchers and practitioners across scientific disciplines to identify the most relevant datasets for a given research problem, a limitation that existing keyword-based dataset search engines struggle to overcome for complex queries. In this paper, we present the system at http://data-hunter.io, which recommends suitable datasets based on research problem descriptions. The system leverages fastText for text representation and classification, the Data Set Knowledge Graph (DSKG) containing metadata for nearly 1,700 unique datasets, and 88,000 paper abstracts as research problem descriptions for model training. Overall, our results demonstrate that dataset recommendation supports data provisioning and reuse in line with the FAIR principles and that dataset recommendation constitutes a promising direction for future research.},
	keywords     = {dataset recommendation, recommender systems, scientific workflows, FAIR data, knowledge graphs},
    pdf          = {publications/DataHunter_RecSys2021.pdf},
tldr = {DataHunter finds datasets from problem descriptions using scholarly text and the Data Set Knowledge Graph, supporting FAIR data discovery workflows.},
plain = {DataHunter searches for datasets from a problem description, like a treasure map that points to the right data by reading what you want to study.}
}

@inproceedings{DBLP:conf/pkdd/NguyenKHF21,
	title        = {Quantifying Explanations of Neural Networks in E-Commerce Based on {LRP}},
	author       = {Anna Nguyen and Franz Krause and Daniel Hagenmayer and Michael F{\"{a}}rber},
	year         = 2021,
	booktitle    = {ECML-PKDD},
	location     = {Bilbao, Spain},
	volume       = 12979,
	pages        = {251--267},
	doi          = {10.1007/978-3-030-86517-7\_16},
	url          = {https://doi.org/10.1007/978-3-030-86517-7\_16},
    abstract     = {Neural networks are widely used in e-commerce, particularly for product recommendation, yet their black-box nature makes it difficult to understand how recommendations are generated, which hinders trust and reliability. In this paper, we present TransPer, an explanation framework for neural networks that introduces novel explanation measures based on Layer-Wise Relevance Propagation and supports heterogeneous data as well as complex architectures composed of multiple interconnected neural networks. We apply and evaluate TransPer in two real-world online shop scenarios and show that the resulting explanations help to assess prediction quality, derive ideas for improving the neural network, gain deeper insights into customer behavior, and satisfy legal requirements such as those mandated by the GDPR.},
	keywords     = {explainable AI, Layer-Wise Relevance Propagation, e-commerce, neural networks, transparency},
    pdf          = {publications/QuantifyingExplanations_ECML2021.pdf},
tldr = {We quantify explanation quality for neural networks in e-commerce settings, helping practitioners assess trust and compliance for recommendation models.},
plain = {We measure how good an AI explanation in online shopping really is, like using a ruler to check whether it stays stable, useful, and trustworthy.}
}

@inproceedings{NoulletPF21,
	title        = {Combining Linking Techniques for Everyone},
	author       = {Kristian Noullet and Samuel Printz and Michael F{\"{a}}rber},
	year         = 2021,
	booktitle    = {ESWC},
	pages        = {88--92},
	doi          = {10.1007/978-3-030-80418-3\_16},
	url          = {https://doi.org/10.1007/978-3-030-80418-3\_16},
    abstract     = {While the path in the field of Entity Linking (EL) has been long and has brought forth a plethora of approaches over the years, many of these are exceedingly difficult to execute for purposes of detailed analysis. In many cases, implementations are available, but are far from being a plug-and-play experience. We present Combining Linking Techniques (CLiT), a framework aimed at executing individual linking techniques as well as complex combinations thereof, with a strong focus on reusability, reproducibility, and comparability of existing systems. Furthermore, we introduce protocols for exchanging sub-pipeline-level information with existing and novel systems, enabling improved out-of-the-box compatibility. Among other use cases, our framework can be employed to consolidate multiple systems in combination with meta-learning approaches and to increase support for backward compatibility of existing benchmark annotation systems.},
	keywords     = {entity linking, reproducibility, evaluation frameworks, pipelines, benchmarking},
    pdf          = {publications/CLiT_ESWC2021.pdf},
tldr = {We provide a framework for combining and comparing entity-linking components, improving reproducibility and fair evaluation across pipelines.},
plain = {We make entity linking plug-and-play, like LEGO bricks that can be swapped and combined so users can compare systems without rebuilding everything.}
}

@inproceedings{DBLP:conf/bias/0001B21,
	title        = {Media Bias Everywhere? {A} Vision for Dealing with the Manipulation of Public Opinion},
	author       = {Michael F{\"{a}}rber and Frederic Bartscherer},
	year         = 2021,
	booktitle    = {BIAS@ECIR},
	location     = {Lucca, Italy},
	pages        = {9--13},
	doi          = {10.1007/978-3-030-78818-6\_2},
	url          = {https://doi.org/10.1007/978-3-030-78818-6\_2},
    abstract     = {This paper addresses the question of how artificial intelligence can be used to detect media bias within the broader context of manipulation and mood-making. We identify three fields of action that emerge from applying machine learning to the analysis of media bias: evaluation principles for media bias, information presentation of media bias, and transparency in media bias evaluation. Practical applications of our findings arise both in professional settings for journalists and publishers and in the everyday lives of citizens. First, automated analysis can be used to analyze texts in real time and promote more balanced reporting. Second, an intuitive web browser application can reveal existing bias in news texts in a way that is accessible to citizens. Finally, in educational contexts, pupils can experience media bias and the use of artificial intelligence in practice, thereby fostering media literacy.},
	keywords     = {media bias, public opinion, misinformation, bias detection, media literacy},
    pdf          = {publications/BiasVisionPaper_BIAS2021.pdf},
tldr = {We outline how AI can detect and communicate media bias, supporting transparency, media literacy, and more resilient public discourse.},
plain = {We sketch a blueprint for fighting manipulation in the news, showing how AI can detect bias and present it in ways people can actually understand.}
}

@inproceedings{DBLP:conf/www/0001ZCCD21,
	title        = {C-Rex: {A} Comprehensive System for Recommending In-Text Citations with Explanations},
	author       = {Michael F{\"{a}}rber and Vinzenz Zinecker and Isabela Bragaglia Cartus and Sebastian Celis and Maria Duma},
	year         = 2021,
	booktitle    = {Sci-K@WWW},
	location     = {Virtual Event, Ljubljana, Slovenia},
	pages        = {441--445},
	doi          = {10.1145/3442442.3451366},
	url          = {https://doi.org/10.1145/3442442.3451366},
    abstract     = {Finding suitable citations for scientific publications can be challenging and time-consuming. To address this issue, context-aware citation recommendation approaches have been developed that recommend publications as candidates for in-text citations. In this paper, we present C-Rex, a web-based demonstration system available at http://c-rex.org for context-aware citation recommendation based on the Neural Citation Network and millions of publications from the Microsoft Academic Graph. Our system is one of the first online context-aware citation recommendation systems and the first to incorporate not only a deep learning–based recommendation approach but also explanation components to help users better understand why papers were recommended. In our offline evaluation, our model performs similarly to the one presented in the original paper and can serve as a basic framework for further implementations. In our online evaluation, we found that providing explanations for recommendations increased users’ satisfaction.},
	keywords     = {citation recommendation, explanations, scholarly writing support, neural models, digital libraries},
    pdf          = {publications/C-Rex_Sci-K-WWW2021.pdf},
    tldr         = {C-Rex is an online system that recommends in-text citations and provides explanations, helping authors write more efficiently and responsibly.},
    plain        = {C-Rex is an online citation assistant, like a helpful co-author that suggests supporting papers and briefly explains why they fit.}
}

@inproceedings{DBLP:conf/ecir/JatowtHFCY21,
	title        = {Exploding {TV} Sets and Disappointing Laptops: Suggesting Interesting Content in News Archives Based on Surprise Estimation},
	author       = {Adam Jatowt and I{-}Chen Hung and Michael F{\"{a}}rber and Ricardo Campos and Masatoshi Yoshikawa},
	year         = 2021,
	booktitle    = {ECIR},
	publisher    = {Springer},
	volume       = 12656,
	pages        = {254--269},
	doi          = {10.1007/978-3-030-72113-8\_17},
	url          = {https://doi.org/10.1007/978-3-030-72113-8\_17},
    abstract     = {Many archival collections have recently been digitized and made available to a wide audience. However, the documents they contain often have limited appeal for ordinary users, as the content may appear obsolete or uninteresting. Archival document collections can become more attractive if suitable content is recommended to users. This work proposes a new research direction, termed Archival Content Suggestion, which aims to discover interesting content from long-term document archives that preserve information about societal history and heritage. To achieve this goal, we introduce two unsupervised approaches for automatically identifying interesting sentences from news article archives. Our methods detect interesting content by comparing information written in the past with information created in the present, thereby exploiting a surprise effect. Experiments on the New York Times corpus demonstrate that our approaches effectively retrieve interesting content.},
    keywords     = {news archives, content recommendation, surprise estimation, archival search, interestingness},
    pdf          = {publications/Exploding_TV_ECIR2020.pdf},
    tldr = {We recommend surprisingly interesting content from news archives by contrasting past and present, making digital heritage collections more engaging.},
    plain = {We find “surprising gems” in news archives by comparing past statements with today, like opening a time capsule and highlighting what changed.}
}

@inproceedings{DBLP:conf/ecir/NguyenOF21,
	title        = {Right for the Right Reasons: Making Image Classification Intuitively Explainable},
	author       = {Anna Nguyen and Adrian Oberf{\"{o}}ll and Michael F{\"{a}}rber},
	year         = 2021,
	booktitle    = {ECIR},
	location     = {Virtual Event},
	volume       = 12657,
	pages        = {327--333},
	doi          = {10.1007/978-3-030-72240-1\_32},
	url          = {https://doi.org/10.1007/978-3-030-72240-1\_32},
    abstract     = {The effectiveness of Convolutional Neural Networks (CNNs) in image classification has been well established. To make classification decisions understandable to humans, various methods for visualizing classification evidence have been developed in recent years. These explanations reveal that images are sometimes classified correctly but for the wrong reasons, that is, based on incidental evidence rather than the actual objects of interest. Ideally, images should be classified correctly for the right reasons, namely based on the relevant evidence. To this end, we propose a new explanation quality metric, termed ObAlEx, which measures object-aligned explanations in image classification. By combining object detection approaches, explanation methods, and the ObAlEx metric, we quantify the extent to which CNNs focus on the actual evidence. Furthermore, we demonstrate that additional training of CNNs can improve this focus without reducing classification accuracy.},
    keywords     = {explainable AI, image classification, explanation quality, object alignment, CNNs},
    pdf          = {publications/Right4Right_ECIR2021.pdf},
tldr = {We propose a metric for object-aligned explanations in image classification and show training can make models rely on the right evidence.},
plain = {We check whether image AIs look at the right evidence, like asking a student to point to the exact part of a picture that supports the answer.}
}

@inproceedings{DBLP:conf/aaai/FarberAS21,
	title        = {Identifying Used Methods and Datasets in Scientific Publications},
	author       = {Michael F{\"{a}}rber and Alexander Albers and Felix Sch{\"{u}}ber},
	year         = 2021,
	booktitle    = {SDU@AAAI},
	url          = {https://ceur-ws.org/Vol-2831/paper19.pdf},
    abstract     = {Although it has become common to assess publications and researchers based on citation counts (e.g., using the h-index), measuring the impact of scientific methods and datasets has so far received limited attention. This is largely due to the fact that information about the usage of methods and datasets is typically not stated explicitly by authors but is instead embedded within the text of publications. In this paper, we propose an approach for identifying methods and datasets in scientific texts that have actually been used by the authors. Our approach first detects dataset and method mentions using a domain-specific named entity recognition technique with minimal human interaction. It then classifies these mentions as used or non-used based on their textual context. The resulting labels are aggregated at the document level and integrated into the Microsoft Academic Knowledge Graph to enrich publication metadata. Experiments on the Microsoft Academic Graph demonstrate that both method and dataset mentions can be accurately identified and correctly classified with respect to their usage. Overall, our approach supports method and dataset recommendation, enhanced paper recommendation, and scientific impact quantification, and can be extended to identify mentions of arbitrary entity types (e.g., tasks).},
    keywords     = {scientific text mining, method extraction, dataset extraction, information extraction, scholarly knowledge graphs},
    pdf          = {publications/Identifying_Methods_and_Datasets_SDU-AAAI-2021.pdf},
tldr = {We identify automatically which methods and datasets were used in papers via extraction and context classification.},
plain = {We extract which methods and datasets papers really used.}
}

@inproceedings{DBLP:conf/semweb/0001L22,
	title        = {The Green {AI} Ontology: An Ontology for Modeling the Energy Consumption of {AI} Models},
	author       = {Michael F{\"{a}}rber and David Lamprecht},
	year         = 2022,
	booktitle    = {ISWC},
	location     = {Virtual Conference, Hangzhou, China},
	publisher    = {CEUR-WS.org},
	volume       = 3254,
	url          = {https://ceur-ws.org/Vol-3254/paper361.pdf},
    abstract     = {Modeling AI systems’ characteristics of energy consumption and their sustainability level as an extension of the FAIR data principles has been considered only rudimentarily. In this paper, we propose the Green AI Ontology for modeling the energy consumption and other environmental aspects of AI models. We evaluate our ontology based on competency questions. Our ontology is available at https://w3id.org/Green-AI-Ontology and can be used in a variety of scenarios, ranging from comprehensive research data management to strategic controlling of institutions and environmental efforts in politics.},
    keywords     = {ontology, Green AI, energy consumption, sustainability, FAIR},
    pdf          = {publications/GreenAI_ISWC2022.pdf},
tldr = {We introduce the Green AI Ontology to represent energy use and sustainability properties of AI models, enabling transparent reporting and comparison.},
plain = {We build a “nutrition label” for AI models that records energy use and sustainability, so systems can be compared with clear facts.}
}

@inproceedings{BlockingJCDL2022,
	title        = {A Blocking-Based Approach to Enhance Large-Scale Reference Linking},
	author       = {Tarek Saier and Meng Luan and Michael F{\"{a}}rber},
	year         = 2022,
	booktitle    = {LITE@JCDL},
	publisher    = {CEUR-WS.org},
	volume       = 3220,
	pages        = {16--25},
	url          = {https://ceur-ws.org/Vol-3220/paper2.pdf},
    abstract     = {Analyses and applications based on bibliographic references are of ever increasing importance. However, reference linking methods described in the literature are only able to link around half of the references in papers. To improve the quality of reference linking in large scholarly data sets, we propose a blocking-based reference linking approach that utilizes a rich set of reference fields (title, author, journal, year, etc.) and is independent of a target collection of paper records to be linked to. We evaluate our approach on a corpus of 300,000 references. Relative to the original data, we achieve a 90\% increase in papers linked through references, a five-fold increase in bibliographic coupling, and a nine-fold increase in in-text citations covered. The newly established links are of high quality (85\% F1).},
    pdf          = {publications/Blocking_JCDL2022.pdf},
    keywords     = {blocking, MAKG, Scholarly Data},
tldr = {We improve large-scale reference linking using a blocking strategy over reference fields.},
plain = {We speed up linking references by sorting them into smart bins first, like organizing mail before matching addresses so more links are made correctly.}
}

@inproceedings{DBLP:conf/jcdl/Nishioka0S22,
	title        = {How Does Author Affiliation Affect Preprint Citation Count? Analyzing Citation Bias at the Institution and Country Level},
	author       = {Chifumi Nishioka and Michael F{\"{a}}rber and Tarek Saier},
	year         = 2022,
	booktitle    = {JCDL},
	location     = {Cologne, Germany},
	pages        = {28:1--28:8},
	doi          = {10.1145/3529372.3530953},
	url          = {https://doi.org/10.1145/3529372.3530953},
    abstract     = {Citing is an important aspect of scientific discourse and essential for quantifying the scientific impact of researchers. Previous works observed that citations are made not only based on scholarly contributions but also based on non-scholarly attributes, such as the affiliation or gender of authors, thereby producing citation bias. Existing research, however, has not analyzed preprints with respect to citation bias, even though they play an increasingly important role in modern scholarly communication. In this paper, we investigate whether preprints are affected by citation bias with respect to author affiliation. We measure citation bias for bioRxiv preprints and their publisher versions at the institution and country levels, using the Lorenz curve and Gini coefficient. This allows us to mitigate the effects of confounding factors and examine whether affiliation-related citation biases have an increased effect on preprint citations. We observe consistently higher Gini coefficients for preprints than for publisher versions. Thus, we can confirm that citation bias exists and is more severe for preprints. As preprints continue to rise in importance, affiliation-based citation bias becomes a relevant topic not only for authors (e.g., when deciding what to cite) but also for stakeholders who use citations for scientific impact quantification, such as funding agencies evaluating researchers based on citation counts.},
    keywords     = {citation bias, preprints, author affiliation, scientometrics, Gini coefficient},
    pdf          = {publications/CitationBias_JCDL2022.pdf},
tldr = {We quantify affiliation-based citation inequality for bioRxiv preprints and show stronger bias signals than in publisher versions.},
plain = {We show that an author’s origin can influence citation outcomes, like a nameplate effect that raises fairness questions in how science is evaluated.}
}

@inproceedings{FSDLRE2022,
	title        = {Few-Shot Document-Level Relation Extraction},
	author       = {Nicholas Popovic and Michael F{\"{a}}rber},
	year         = 2022,
	booktitle    = {NAACL},
	location     = {Seattle, WA, USA},
	pages        = {5733--5746},
	doi          = {10.18653/V1/2022.NAACL-MAIN.421},
	url          = {https://doi.org/10.18653/v1/2022.naacl-main.421},
    abstract = {We present FREDo, a few-shot document-level relation extraction (FSDLRE) benchmark. As opposed to existing benchmarks which are built on sentence-level relation extraction corpora, we argue that document-level corpora provide more realism, particularly regarding none-of-the-above (NOTA) distributions. Therefore, we propose a set of FSDLRE tasks and construct a benchmark based on two existing supervised learning data sets, DocRED and sciERC. We adapt the state-of-the-art sentence-level method MNAV to the document-level and develop it further for improved domain adaptation. We find FSDLRE to be a challenging setting with interesting new characteristics such as the ability to sample NOTA instances from the support set. The data, code, and trained models are available online.},
    keywords = {relation extraction, NLP},
    pdf = {publications/FSDLRE_NAACL2022.pdf},
tldr = {We introduce a few-shot benchmark for document-level relation extraction and reveal challenges beyond sentence-level settings, including realistic NOTA behavior.},
plain = {We create a benchmark for extracting relationships from whole documents with only a few examples, like testing whether a student understood the full story and not just one line.},
selected = {true}
}

@inproceedings{SemEval2022,
	title        = {AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First - Using Relation Extraction to Identify Entities},
	author       = {Nicholas Popovic and Walter Laurito and Michael F{\"{a}}rber},
	year         = 2022,
	booktitle    = {SemEval@NAACL},
	pages        = {1687--1694},
	doi          = {10.18653/V1/2022.SEMEVAL-1.232},
	url          = {https://doi.org/10.18653/v1/2022.semeval-1.232},
    abstract  = {In this paper, we present an end-to-end joint entity and relation extraction approach based on transformer-based language models. We apply the model to the task of linking mathematical symbols to their descriptions in LaTeX documents. In contrast to existing approaches, which perform entity and relation extraction in sequence, our system incorporates information from relation extraction into entity extraction. This means that the system can be trained even on data sets where only a subset of all valid entity spans is annotated. We provide an extensive evaluation of the proposed system and its strengths and weaknesses. Our approach, which can be scaled dynamically in computational complexity at inference time, produces predictions with high precision and reaches 3rd place in the leaderboard of SemEval-2022 Task 12. For inputs in the domain of physics and math, it achieves high relation extraction macro F1 scores of 95.43\% and 79.17\%, respectively. The code used for training and evaluating our models is available at: https://github.com/nicpopovic/RE1st.},
    pdf = {publications/RE1st_SemEval2022.pdf},
    keywords = {relation extraction, NLP},
tldr = {We build an end-to-end pipeline where relation extraction supports entity identification, improving extraction from LaTeX documents in a shared-task setting.},
plain = {We help find names in technical text by learning relationships first.}
}

@inproceedings{DBLP:conf/birws/00010PSN22,
	title        = {Which Publications' Metadata Are in Which Bibliographic Databases? {A} System for Exploration},
	author       = {Michael F{\"{a}}rber and Christoph Braun and Nicholas Popovic and Tarek Saier and Kristian Noullet},
	year         = 2022,
	booktitle    = {BIR@ECIR},
	location     = {Stavanger, Norway},
	volume       = 3230,
	pages        = {39--44},
	url          = {https://ceur-ws.org/Vol-3230/paper-06.pdf},
	pdf          = {publications/RefBee_BIR-ECIR2022.pdf},
	biburl       = {https://dblp.org/rec/conf/birws/00010PSN22.bib},
	abstract     = {The choice of bibliographic databases containing publication metadata determines the available publication list of any author and thus their public appearance and evaluation. Ensuring that all publications are listed across databases is therefore important for researchers. However, as the average number of publications per researcher steadily rises, it becomes labor-intensive and time-consuming to verify whether all publications appear in all major databases. In this paper, we present RefBee, an online system that retrieves publication metadata for a given author from multiple bibliographic databases and indicates which publications are missing in which database. RefBee, available at http://refbee.org/, currently supports Wikidata, ORCID, Google Scholar, VIAF, DBLP, Dimensions, Microsoft Academic, Semantic Scholar, and DNB/GNB. The system can serve as an assistance tool for more than 4.7 million researchers across disciplines and languages, while also incentivizing the use and population of Wikidata in the scholarly domain.},
	keywords     = {bibliographic databases, publication metadata, author profiles, scholarly infrastructure, Wikidata},
tldr = {RefBee helps researchers check which publications are missing from major bibliographic databases, improving visibility and metadata completeness.},
plain = {RefBee is a checklist for your publication record, like spotting missing books on a shelf before others do.}
}
 
@inproceedings{MullerBF22,
	title        = {Safety Aware Reinforcement Learning by Identifying Comprehensible Constraints in Expert Demonstrations},
	author       = {Leopold M{\"{u}}ller and Lars B{\"{o}}cking and Michael F{\"{a}}rber},
	year         = 2022,
	booktitle    = {SafeAI@AAAI},
	url          = {https://ceur-ws.org/Vol-3087/paper\_14.pdf},
	biburl       = {https://dblp.org/rec/conf/aaai/MullerBF22.bib},
	abstract     = {When used in real-world environments, agents must meet high safety requirements as errors have direct consequences. Besides safety, the explainability of the systems is particularly important: errors should not only be avoided during learning, but the decision process must also be transparent. Existing approaches are limited to solving a single problem, whereas real-world scenarios require satisfying several criteria simultaneously. In this paper, we derive comprehensible rules from expert demonstrations that can be used to monitor the agent. The approach uses state-of-the-art classification and regression trees to derive safety rules, combined with concepts from association rule mining. The result is a compact and interpretable rule set that explains expert behavior and ensures safety. We evaluate our framework in common OpenAI environments, showing that it identifies safety-relevant rules and effectively imitates expert behavior, especially in edge cases. Further evaluations in higher-dimensional observation spaces and continuous action spaces demonstrate that the approach transfers well to new tasks while maintaining the compactness and comprehensibility of the rule set.},
	keywords     = {safe reinforcement learning, interpretability, expert demonstrations, rule extraction, constraint learning},
    pdf          = {publications/Safety-Aware_RL_SafeAI-AAAI2022.pdf},
tldr = {We learn human-readable safety constraints from expert demonstrations to monitor RL agents, improving safety without losing interpretability.},
plain = {We turn expert demonstrations into clear safety rules, like writing down a skilled driver’s habits so an AI can avoid dangerous moves.}
}

@inproceedings{WhenNN2022,
	title        = {When to Use Which Neural Network? Finding the Right Neural Network Architecture for a Research Problem},
	author       = {Michael F{\"{a}}rber and Nicolas Weber},
	year         = 2022,
	booktitle    = {SDU@AAAI},
	url          = {https://ceur-ws.org/Vol-3164/paper13.pdf},
	biburl       = {https://dblp.org/rec/conf/aaai/0001W22.bib},
	abstract     = {Considering the increasing rate of scientific papers published in recent years, researchers across all disciplines face the challenge of keeping track of which latest scientific methods are suitable for which applications. In particular, an unmanageable number of neural network architectures has been introduced. In this paper, we propose the task of recommending neural network architectures based on textual problem descriptions. We frame this recommendation as a text classification task and develop appropriate text classification models for it. In experiments based on three datasets, we find that an SVM classifier outperforms a more complex model based on BERT. Overall, we provide evidence that neural network architecture recommendation is a nontrivial yet promising research topic.},
	keywords     = {architecture recommendation, neural networks, text classification, scientific literature mining, method recommendation},
    pdf          = {publications/NNARec_SDU-AAAI2022.pdf},
tldr = {We recommend neural-network architectures from textual problem descriptions and show that simpler models can be strong baselines for this task.},
plain = {We recommend neural-network designs from a short problem description, like a tool clerk suggesting the right instrument once you describe the job.}
}

@inproceedings{noullet2023full,
	title        = {A Full-Fledged Framework for Combining Entity Linking Systems and Components},
	author       = {Kristian Noullet and Ayoub Ourgani and Michael Färber},
	year         = 2023,
	booktitle    = {K-CAP},
	doi          = {10.1145/3587259.3627556},
	url          = {https://dl.acm.org/doi/10.1145/3587259.3627556},
	abstract  = {Named entity recognition and disambiguation, often referred to as entity linking systems, refers to the task of automatically identifying knowledge graph entities in text documents. While a variety of entity linking systems based on very different approaches exist, these systems implicitly share certain processing steps in their pipeline. Despite this fact, they have been mainly used as stand-alone solutions. In this paper, we propose a framework for combining entity linking methods. This allows multiple entity linking systems and especially their components to be used in combination to an unlimited extent, thus allowing to achieve the best possible performance. In addition, the framework allows user-developed entity linking systems or components to be easily tested and automatically evaluated against other systems without having to set up other systems first. Essentially, our framework is knowledge graph agnostic and entity linking systems can be compared across knowledge graphs. Furthermore, our framework enables entity linking method or component recommendation, supporting the goal of achieving the best performance in a given context. We demonstrate that non-domain-expert users are able to deploy the framework within minutes and integrate unknown homebrew systems into it in less than an hour. Our framework is fully open source and available on GitHub along with Docker containers and tutorials (including Jupyter Notebooks).},
    pdf = {publications/EL_K-CAP2023.pdf},
    keywords = {entity linking},
tldr = {We present a full framework for modular entity-linking pipelines, enabling easier combination of components and fairer comparison across systems.},
plain = {We offer a framework to combine entity-linking systems, like a mixing console where you can plug in different components and compare results fairly.}
}

@inproceedings{DBLP:conf/compsac/FaragoFP23,
	title        = {A Full-fledged Commit Message Quality Checker Based on Machine Learning},
	author       = {David Farag{\'{o}} and Michael F{\"{a}}rber and Christian Petrov},
	year         = 2023,
	booktitle    = {COMPSAC},
	location     = {Torino, Italy},
	pages        = {788--799},
	doi          = {10.1109/COMPSAC57700.2023.00108},
	url          = {https://doi.org/10.1109/COMPSAC57700.2023.00108},
	abstract  = {Commit messages (CMs) are an essential part of version control. By providing important context regarding what has changed and why, they strongly support software maintenance and evolution. However, writing good CMs is difficult and often neglected by developers. So far, there is no tool suitable for practice that automatically assesses how well a CM is written, including its meaning and context. Since this task is challenging, we ask the research question: how well can commit message quality, including semantics and context, be measured with machine learning methods? By considering all rules from the most popular CM quality guideline, creating datasets for those rules, and training and evaluating state-of-the-art machine learning models to check these rules, we can answer the research question with: sufficiently well for practice, with the lowest F1 score of 82.9\% for the most challenging task. We develop a full-fledged open-source framework that checks all these CM quality rules. It is useful for research (e.g., automatic CM generation), but most importantly for software practitioners to raise the quality of CMs and thus the maintainability and evolution speed of their software.},
    keywords     = {Commit Messages, Software Engineering, Software Quality},
    pdf = {publications/Commit-Message-Quality-Checker_COMPSAC2023.pdf},
tldr = {We build an ML-based tool that checks commit-message quality using established rules, supporting maintainability and better software engineering practice.},
plain = {We build a quality checker for commit messages, like a grammar checker for software teams so code changes stay understandable.}
}

@inproceedings{Readability2023,
	title        = {Ablesbarkeitsmesser: {A} System for Assessing the Readability of German Text},
	author       = {Florian Pickelmann and Michael F{\"{a}}rber and Adam Jatowt},
	year         = 2023,
	booktitle    = {ECIR},
	location     = {Dublin, Ireland},
	pages        = {288--293},
	doi          = {10.1007/978-3-031-28241-6\_28},
	url          = {https://doi.org/10.1007/978-3-031-28241-6\_28},
    abstract  = {While several approaches have been proposed for estimating the readability of English texts, there is much less work for other languages. In this paper, we present an online service, available at https://readability-check.org/, that provides five well-established statistical methods and two machine learning models for measuring the readability of texts in German. For the machine learning methods, we train two BERT models. To bring all the measures together, we provide an interactive website that allows users to evaluate the readability of German texts at the sentence level. Our research can be useful for anyone who wants to know whether the text content at hand is easy or difficult and therefore can be used in certain situations or rather needs to be adapted and improved. In education, for example, it can help to assess the suitability of a particular teaching material for a particular grade.},
    keywords  = {Readability, German Language Processing, Text Difficulty, Text Assessment},
    pdf = {publications/Readability_ECIR2023.pdf},
tldr = {We provide an online service to estimate readability of German texts, supporting clearer writing for education and public communication.},
plain = {We build a “readability thermometer” for German, so people can quickly see whether a text is easy or hard to read and adjust it for the audience.}
}

@inproceedings{regev2024future,
	title        = {Future Timelines: Extraction and Visualization of Future-related Content From News Articles},
	author       = {Juwal Regev and Adam Jatowt and Michael Färber},
	year         = 2024,
	booktitle    = {WSDM},
	address      = {Merida, Mexico},
	doi          = {10.1145/3616855.3635693},
	url          = {https://dl.acm.org/doi/abs/10.1145/3616855.3635693},
	abstract     = {In today's rapidly evolving world, maintaining a comprehensive overview of the future landscape is essential for staying competitive and making informed decisions. However, given the largevolume of daily news, manually obtaining a thorough overview ofan entity’s future prospects is quite challenging. To address this, wepresent a system designed to automatically extract and summarizefuture-related information of a queried entity from news articles. Our approach utilizes a novel and publicly accessible multi-sourcedataset comprising 6,800 annotated sentences to fine-tune a language model to identify future-related sentences. We then use topicmodeling to extract the main topics from the data and rank themby relevance as well as present them on an interactive timeline. User evaluations have shown that the timelines and summariesour system produces are useful. The system is available as a webapplication at: https://chronicle2050.regevson.com.},
	keywords     = {future, demo, timeline},
	pdf          = {publications/Future-Timelines_WSDM2024.pdf},
tldr = {We extract future-oriented statements from news and visualize them on timelines, helping users anticipate developments under daily information overload.},
plain = {We pull future-looking statements from news and place them on a timeline, like turning daily headlines into a simple roadmap of what might come next.}
}

@inproceedings{EMBEREMNLP2024,
	title        = {Embedded Named Entity Recognition using Probing Classifiers},
	author       = {Nicholas Popovic and Michael F{\"{a}}rber},
	year         = 2024,
	booktitle    = {EMNLP},
	location     = {Miami, FL, USA},
	pages        = {17830--17850},
	url          = {https://aclanthology.org/2024.emnlp-main.988},
	abstract     = {Streaming text generation has become a common way of increasing the responsiveness of language model powered applications, such as chat assistants. At the same time, extracting semantic information from generated text is a useful tool for applications such as automated fact checking or retrieval augmented generation. Currently, this requires either separate models during inference, which increases computational cost, or destructive fine-tuning of the language model. Instead, we propose an approach called EMBER which enables streaming named entity recognition in decoder-only language models without fine-tuning them and while incurring minimal additional computational cost at inference time. Specifically, our experiments show that EMBER maintains high token generation rates, with only a negligible decrease in speed of around 1% compared to a 43.64% slowdown measured for a baseline. We make our code and data available online1, including a toolkit2 for training, testing, and deploying efficient token classification models optimized for streaming text generation.},
	pdf          = {publications/EMBER_EMNLP2024.pdf},
    keywords = {Named Entity Recognition, Probing Classifiers, Streaming Text Generation, Language Models},
tldr = {EMBER enables fast NER in decoder-only language models via probing, adding minimal overhead and avoiding destructive fine-tuning.},
plain = {We add “live labels” to a chatbot as it writes, like sticky notes appearing while you type instead of only after the text is finished.},
selected = {true}
}

@inproceedings{DBLP:conf/kbclm/RogulskyP024,
	title        = {The Effects of Hallucinations in Synthetic Training Data for Relation Extraction},
	author       = {Steven Rogulsky and Nicholas Popovic and Michael F{\"{a}}rber},
	year         = 2024,
	booktitle    = {KBC-LM@ISWC},
	location     = {Baltimore, USA},
	publisher    = {CEUR-WS.org},
	url          = {https://ceur-ws.org/Vol-3853/paper4.pdf},
	biburl       = {https://dblp.org/rec/conf/kbclm/RogulskyP024.bib},
	abstract     = {Relation extraction is crucial for constructing knowledge graphs, with large high-quality datasets serving as the foundation for training, fine-tuning, and evaluating models. Generative data augmentation (GDA) is a common approach to expand such datasets. However, this approach often introduces hallucinations, such as spurious facts, whose impact on relation extraction remains underexplored. In this paper, we examine the effects of hallucinations on the performance of relation extraction on the document and sentence levels. Our empirical study reveals that hallucinations considerably compromise the ability of models to extract relations from text, with recall reductions between 19.1% and 39.2%. We identify that relevant hallucinations impair the model’s performance, while irrelevant hallucinations have a minimal impact. Additionally, we develop methods for the detection of hallucinations to improve data quality and model performance. Our approaches successfully classify texts as either ’hallucinated’ or ’clean,’ achieving high F1-scores of 83.8% and 92.2%. These methods not only assist in removing hallucinations but also help in estimating their prevalence within datasets, which is crucial for selecting high-quality data. Overall, our work confirms the profound impact of relevant hallucinations on the effectiveness of relation extraction models.},
	keywords     = {relation extraction, synthetic data, hallucinations, data quality, knowledge graphs},
	pdf          = {publications/Hallucinations_KBC-LM_ISWC2024.pdf},
tldr = {We show that hallucinated synthetic training data can seriously reduce relation extraction quality and propose detectors to filter hallucinations.},
plain = {We show that synthetic training data can be polluted by made-up details, like feeding a model mislabeled food, and we propose ways to detect and clean it.}
}

@inproceedings{DBLP:conf/semweb/SusantiF24,
	title        = {Knowledge Graph Structure as Prompt: Improving Small Language Models Capabilities for Knowledge-Based Causal Discovery},
	author       = {Yuni Susanti and Michael F{\"{a}}rber},
	year         = 2024,
	booktitle    = {ISWC},
	location     = {Baltimore, MD, USA},
	publisher    = {Springer},
	pages        = {87--106},
	doi          = {10.1007/978-3-031-77844-5\_5},
	url          = {https://doi.org/10.1007/978-3-031-77844-5\_5},
	biburl       = {https://dblp.org/rec/conf/semweb/SusantiF24.bib},
	abstract     = {Causal discovery aims to estimate causal structures among variables based on observational data. Large Language Models (LLMs) offer a fresh perspective to tackle the causal discovery problem by reasoning on the metadata associated with variables rather than their actual data values, an approach referred to as knowledge-based causal discovery. In this paper, we investigate the capabilities of Small Language Models (SLMs, defined as LLMs with fewer than 1 billion parameters) with prompt-based learning for knowledge-based causal discovery. Specifically, we present “KG Structure as Prompt”, a novel approach for integrating structural information from a knowledge graph, such as common neighbor nodes and metapaths, into prompt-based learning to enhance the capabilities of SLMs. Experimental results on three types of biomedical and open-domain datasets under few-shot settings demonstrate the effectiveness of our approach, surpassing most baselines and even conventional fine-tuning approaches trained on full datasets. Our findings further highlight the strong capabilities of SLMs: in combination with knowledge graphs and prompt-based learning, SLMs demonstrate the potential to surpass LLMs with larger number of parameters. Our code and datasets are available on GitHub.},
	keywords     = {causal discovery, small language models, knowledge graphs, prompt-based learning, metapaths},
	pdf          = {publications/KG_Structure_Prompt_ISWC2024.pdf},
tldr = {We inject knowledge graph structure into prompts to improve small language models for causal discovery, showing strong few-shot gains over baselines.},
plain = {We help smaller language models think about causes by giving them a structured knowledge map, like signposts that reduce guessing in the dark.}
}

@inproceedings{DBLP:conf/semweb/FarberLS24,
	title        = {AutoRDF2GML: Facilitating {RDF} Integration in Graph Machine Learning},
	author       = {Michael F{\"{a}}rber and David Lamprecht and Yuni Susanti},
	year         = 2024,
	booktitle    = {ISWC},
	location     = {Baltimore, MD, USA},
	publisher    = {Springer},
	pages        = {115--133},
	doi          = {10.1007/978-3-031-77847-6\_7},
	url          = {https://doi.org/10.1007/978-3-031-77847-6\_7},
	biburl       = {https://dblp.org/rec/conf/semweb/FarberLS24.bib},
	abstract     = {In this paper, we introduce AutoRDF2GML, a framework designed to convert RDF data into data representations tailored for graph machine learning tasks. AutoRDF2GML enables, for the first time, the creation of both content-based features—i.e., features based on RDF datatype properties—and topology-based features—i.e., features based on RDF object properties. Characterized by automated feature extraction, AutoRDF2GML makes it possible even for users less familiar with RDF and SPARQL to generate data representations ready for graph machine learning tasks, such as link prediction, node classification, and graph classification. Furthermore, we present four new benchmark datasets for graph machine learning, created from large RDF knowledge graphs using our framework. These datasets serve as valuable resources for evaluating graph machine learning approaches, such as graph neural networks. Overall, our framework effectively bridges the gap between the Graph Machine Learning and Semantic Web communities, paving the way for RDF-based machine learning applications.},
	keywords     = {RDF, graph machine learning, feature extraction, knowledge graphs, benchmarks},
	pdf          = {publications/AutoRDF2GML_ISWC24.pdf},
tldr = {AutoRDF2GML turns RDF knowledge graphs into graph-ML-ready datasets with content and topology features, bridging semantic web data and graph learning.},
plain = {AutoRDF2GML converts knowledge graphs into machine learning datasets, like turning a city map into a format a navigation system can train on.}
}

@inproceedings{DBLP:conf/acl/YuanN0SS24,
	title        = {GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network},
	author       = {Shuzhou Yuan and Ercong Nie and Michael F{\"{a}}rber and Helmut Schmid and Hinrich Sch{\"{u}}tze},
	year         = 2024,
	booktitle    = {ACL},
	location     = {Bangkok, Thailand},
	publisher    = {Association for Computational Linguistics},
	pages        = {3987--4001},
	doi          = {10.18653/V1/2024.FINDINGS-ACL.237},
	url          = {https://doi.org/10.18653/v1/2024.findings-acl.237},
	editor       = {Lun{-}Wei Ku and Andre Martins and Vivek Srikumar},
	timestamp    = {Tue, 24 Sep 2024 10:55:54 +0200},
	biburl       = {https://dblp.org/rec/conf/acl/YuanN0SS24.bib},
	abstract     = {Large Language Models (LLMs) exhibit strong In-Context Learning (ICL) capabilities when prompts with demonstrations are used. However, fine-tuning still remains crucial to further enhance their adaptability. Prompt-based fine-tuning proves to be an effective fine-tuning method in low-data scenarios, but high demands on computing resources limit its practicality. We address this issue by introducing a prompt-based parameter-efficient finetuning (PEFT) approach. GNNAVI leverages insights into ICL’s information flow dynamics, which indicates that label words act in prompts as anchors for information propagation. GNNAVI employs a Graph Neural Network (GNN) layer to precisely guide the aggregation and distribution of information flow during the processing of prompts by hardwiring the desired information flow into the GNN. Our experiments on text classification tasks with GPT-2 and Llama2 show GNNAVI surpasses standard prompt-based fine-tuning methods in few-shot settings by updating just 0.2% to 0.5% of parameters. We compare GNNAVI with prevalent PEFT approaches, such as prefix tuning, LoRA and Adapter in terms of performance and efficiency. Our analysis reveals that GNNAVI enhances information flow and ensures a clear aggregation process.},
	keywords     = {parameter-efficient fine-tuning, graph neural networks, in-context learning, prompt tuning, large language models},
	pdf          = {publications/GNNavi_ACL2024.pdf},
tldr = {GNNavi guides information flow in prompt-based fine-tuning via a graph neural layer, improving few-shot learning while updating only a small fraction of parameters.},
plain = {GNNavi steers how information flows during prompting, like a traffic controller that routes signals to the right places so the model learns better.},
selected = {true}
}

@inproceedings{DBLP:conf/naacl/YuanF24,
	title        = {GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanism},
	author       = {Shuzhou Yuan and Michael F{\"{a}}rber},
	year         = 2024,
	booktitle    = {NAACL},
	location     = {Mexico City, Mexico},
	publisher    = {Association for Computational Linguistics},
	pages        = {920--933},
	doi          = {10.18653/V1/2024.FINDINGS-NAACL.58},
	url          = {https://doi.org/10.18653/v1/2024.findings-naacl.58},
	biburl       = {https://dblp.org/rec/conf/naacl/YuanF24.bib},
	abstract     = {Pretrained Language Models (PLMs) benefit from external knowledge stored in graph structures for various downstream tasks. However, bridging the modality gap between graph structures and text remains a significant challenge. Traditional methods like linearizing graphs for PLMs lose vital graph connectivity, whereas Graph Neural Networks (GNNs) require cumbersome processes for integration into PLMs. In this work, we propose a novel graphguided self-attention mechanism, GraSAME. GraSAME seamlessly incorporates token-level structural information into PLMs without necessitating additional alignment or concatenation efforts. As an end-to-end, lightweight multimodal module, GraSAME follows a multitask learning strategy and effectively bridges the gap between graph and textual modalities, facilitating dynamic interactions between GNNs and PLMs. Our experiments on the graph-to-text generation task demonstrate that GraSAME outperforms baseline models and achieves results comparable to state-of-the-art (SOTA) models on WebNLG datasets. Furthermore, compared to SOTA models, GraSAME eliminates the need for extra pre-training tasks to adjust graph inputs and reduces the number of trainable parameters by over 100 million},
	keywords     = {graph-guided self-attention, graph-to-text generation, pretrained language models, multimodal learning, structural information},
	pdf          = {publications/GraSAME_NAACL2024.pdf},
tldr = {GraSAME injects token-level graph structure into pretrained language models via graph-guided self-attention for efficient graph-to-text generation.},
plain = {GraSAME weaves relationship structure into how a language model reads, like stitching a network into its attention so it can describe connected data more accurately.}
}

@inproceedings{DBLP:conf/icwe/FarberZW24,
	title        = {KITspotlight: {A} System for Spotlighting Researchers in the Media},
	author       = {Michael F{\"{a}}rber and Benjamin Zagoruiko and Markus Wambach},
	year         = 2024,
	booktitle    = {ICWE},
	location     = {Tampere, Finland},
	publisher    = {Springer},
	volume       = 14629,
	pages        = {385--388},
	doi          = {10.1007/978-3-031-62362-2\_29},
	timestamp    = {Thu, 04 Jul 2024 22:05:21 +0200},
	biburl       = {https://dblp.org/rec/conf/icwe/FarberZW24.bib},
	abstract     = {Academic institutions, such as universities, heavily rely on public relations and are therefore interested in media monitoring. However, tracking mentions of their researchers in news articles presents challenges, such as identifying affiliated personnel and their departments, and aggregating the extracted information. In this paper, we introduce KITspotlight, a novel system that automatically identifies researchers of the Karlsruhe Institute of Technology (KIT) in newspaper articles, associates these individuals with their departments, and presents this information visually. KITspotlight is tailored for department heads, administrative staff, and individual researchers, focusing either on the institution’s overall public visibility or on an individual researcher’s media presence. Based on an analysis of 2,280 articles over 12 months, our system provides a model for monitoring academic personnel at any research institution.},
	keywords     = {media monitoring, researcher identification, academic PR, information extraction, dashboard},
	pdf          = {publications/KITspotlight_ICWE2024.pdf},
tldr = {KITspotlight identifies and aggregates researchers mentioned in news articles, enabling scalable media monitoring and visibility analytics for institutions.},
plain = {KITspotlight scans news for mentions of researchers, like a radar that turns scattered articles into a clear visibility dashboard.}
}

@inproceedings{DBLP:conf/eacl/MaNYSFKS24,
	title        = {ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks},
	author       = {Bolei Ma and Ercong Nie and Shuzhou Yuan and Helmut Schmid and Michael F{\"{a}}rber and Frauke Kreuter and Hinrich Sch{\"{u}}tze},
	year         = 2024,
	booktitle    = {EACL},
	location     = {Malta},
	publisher    = {Association for Computational Linguistics},
	pages        = {2685--2702},
	url          = {https://aclanthology.org/2024.eacl-long.164},
	biburl       = {https://dblp.org/rec/conf/eacl/MaNYSFKS24.bib},
	abstract     = {Prompt-based methods have been successfully applied to multilingual pretrained language models for zero-shot cross-lingual understanding. However, most previous studies primarily focused on sentence-level classification tasks, and only a few considered token-level labeling tasks such as Named Entity Recognition (NER) and Part-of-Speech (POS) tagging. In this paper, we propose Token-Level Prompt Decomposition (TOPRO), which facilitates the prompt-based method for token-level sequence labeling tasks. The TOPRO method decomposes an input sentence into single tokens and applies one prompt template to each token. Our experiments on multilingual NER and POS tagging datasets demonstrate that TOPRO-based fine-tuning outperforms Vanilla fine-tuning and Prompt-Tuning in zero-shot cross-lingual transfer, especially for languages that are typologically different from the source language English. Our method also attains state-of-theart performance when employed with the mT5 model. Besides, our exploratory study in multilingual large language models shows that TOPRO performs much better than the current in-context learning method. Overall, the performance improvements show that TOPRO could potentially serve as a novel and simple benchmarking method for sequence labeling tasks},
	keywords     = {cross-lingual transfer, sequence labeling, prompt learning, multilingual NER, POS tagging},
	pdf          = {publications/ToPro_EACL2024.pdf},
tldr = {ToPro decomposes prompts token-by-token for sequence labeling, improving cross-lingual transfer for tasks like NER and POS tagging.},
plain = {ToPro breaks prompts into tiny word-level questions, like asking about each word in turn, which makes labeling across languages less brittle.}
}

@inproceedings{DBLP:conf/ecir/SaierOAF24,
	title        = {HyperPIE: Hyperparameter Information Extraction from Scientific Publications},
	author       = {Tarek Saier and Mayumi Ohta and Takuto Asakura and Michael F{\"{a}}rber},
	year         = 2024,
	booktitle    = {ECIR},
	location     = {Glasgow, UK},
	publisher    = {Springer},
	pages        = {254--269},
	doi          = {10.1007/978-3-031-56060-6\_17},
	url          = {https://doi.org/10.1007/978-3-031-56060-6\_17},
	biburl       = {https://dblp.org/rec/conf/ecir/SaierOAF24.bib},
	abstract     = {Automatic extraction of information from publications is key to making scientific knowledge machine readable at a large scale. The extracted information can, for example, facilitate academic search, decision making, and knowledge graph construction. An important type of information not covered by existing approaches is hyperparameters. In this paper, we formalize and tackle hyperparameter information extraction (HyperPIE) as an entity recognition and relation extraction task. We create a labeled data set covering publications from a variety of computer science disciplines. Using this data set, we train and evaluate BERT-based fine-tuned models as well as five large language models: GPT-3.5, GALACTICA, Falcon, Vicuna, and WizardLM. For fine-tuned models, we develop a relation extraction approach that achieves an improvement of 29\% F1 over a state-of-the-art baseline. For large language models, we develop an approach leveraging YAML output for structured data extraction, which achieves an average improvement of 5.5\% F1 in entity recognition over using JSON. With our best performing model we extract hyperparameter information from a large number of unannotated papers, and analyze patterns across disciplines. All our data and source code is publicly available at \url{https://github.com/IllDepence/hyperpie}.},
	keywords     = {hyperparameter extraction, scientific information extraction, entity recognition, relation extraction, large language models},
	pdf          = {publications/HyperPIE_ECIR2024.pdf},
tldr = {HyperPIE extracts hyperparameters from scientific papers, enabling machine-readable experiment settings for reproducibility, search, and meta-analysis.},
plain = {HyperPIE reads papers to extract exact experiment settings, like pulling oven temperature and timing from thousands of recipes so results can be compared.}
}

@inproceedings{DBLP:conf/ofc/ShaoGLLFKR24,
	title        = {A Novel Machine Learning-based Equalizer for a Downstream 100G {PAM-4} {PON}},
	author       = {Chen Shao and Elias G. Giacoumidis and Shi Li and Jialei Li and Michael F{\"{a}}rber and Tobias K{\"{a}}fer and Andr{\'{e}} Richter},
	year         = 2024,
	booktitle    = {OFC},
	location     = {San Diego, CA, USA},
	publisher    = {{IEEE}},
	pages        = {1--3},
	url          = {https://ieeexplore.ieee.org/document/10526945},
	biburl       = {https://dblp.org/rec/conf/ofc/ShaoGLLFKR24.bib},
	abstract     = {A frequency-calibrated SCINet (FC-SCINet) equalizer is proposed for downstream 100G PON with 28.7 dB path loss. At 5 km, FC-SCINet improves the BER by 88.87\% compared to FFE and a 3-layer DNN with 10.57\% lower complexity.},
	keywords     = {PON, PAM-4, equalization, optical communications, machine learning},
	pdf          = {publications/SCINet_OFC2024.pdf},
tldr = {We propose an ML-based equalizer for downstream 100G PAM-4 PON and show large BER improvements with lower complexity than baselines.},
plain = {We use AI to clean up fiber signals, like noise-cancelling headphones for optical networks, so high-speed links work with fewer errors.}
}

@inproceedings{aue2025predicting,
	title        = {Predicting Company ESG Ratings from News Articles Using Multivariate Time-series Analysis},
	author       = {Tanja Aue and Adam Jatowt and Michael Färber},
	year         = 2025,
	booktitle    = {TempWeb@WWW},
	location     = {Sydney, Australia},
	url          = {https://arxiv.org/abs/2212.11765},
	abstract     = {In recent years, corporate environmental, social, and governance (ESG) engagement has received significant public attention. As mandatory ESG reporting is increasingly adopted and investors place greater emphasis on sustainability in their decisions, the demand for transparent and reliable ESG ratings is growing. However, existing automatic approaches to ESG rating prediction remain limited. Many rely on traditional machine learning methods like random forests or social network analysis, rather than leveraging incoming news article streams and large multivariate time series data, which, for the first time, enables capturing the dynamic relationships between topics, sentiments, and events. In this paper, we propose a novel approach to predicting ESG ratings from news articles by uniquely combining multivariate time series construction with advanced deep learning techniques. We create an extensive dataset of 3.7 million news articles spanning three years and covering 3,000 U.S. companies, providing a robust foundation for training and evaluating our approach. Our approach achieves high accuracy and outperforms existing approaches, underscoring its potential as a scalable, data-driven solution for ESG rating prediction.},
	biburl       = {https://dblp.org/rec/conf/www/AueJ025.bib},
	keywords     = {ESG ratings, news analysis, multivariate time series, deep learning, sustainability analytics},
	pdf          = {publications/ESG_Rating_WWW2025.pdf},
tldr = {We predict company ESG ratings from large news streams using multivariate time-series models, enabling more data-driven sustainability signals at scale.},
plain = {We predict ESG ratings from large streams of news, like building a sustainability dashboard that learns from what companies do and what is reported about them over time.}
}

@inproceedings{färber2025simplifymytext,
	title        = {SimplifyMyText: An LLM-Based System for Inclusive Plain Language Text Simplification},
	author       = {Michael Färber and Parisa Aghdam and Kyuri Im and Mario Tawfelis and Hardik Ghoshal},
	year         = 2025,
	booktitle    = {ECIR},
	location     = {Lucca,Italy},
	keywords     = {text simplification, plain language, large language models, accessibility, information retrieval},
	biburl       = {https://dblp.org/rec/conf/ecir/FarberAITG25.bib},
	url          = {https://link.springer.com/chapter/10.1007/978-3-031-88717-8_32},
	abstract     = {Text simplification is essential for making complex content accessible to diverse audiences who face comprehension challenges. Yet, the limited availability of simplified materials creates significant barriers to personal and professional growth and hinders social inclusion. Although researchers have explored various methods for automatic text simplification, none fully leverage large language models (LLMs) to offer tailored customization for different target groups and varying levels of simplicity. Moreover, despite its proven benefits for both consumers and organizations, the well-established practice of plain language remains underutilized. In this paper, we introduce https://simplifymytext.org, the first system designed to produce plain language content from multiple input formats, including typed text and file uploads, with flexible customization options for diverse audiences. We employ GPT-4 and Llama-3 and evaluate outputs across multiple metrics. Overall, our work contributes to research on automatic text simplification and highlights the importance of tailored communication in promoting inclusivity.},
	pdf          = {publications/SimplifyMyText_ECIR2025.pdf},
tldr = {We build and evaluate an LLM system that turns complex documents into inclusive plain language, improving accessibility for readers with comprehension barriers.},
plain = {SimplifyMyText turns dense documents into plain language, like clearing bureaucratic fog so more people can understand what matters.}
}

@inproceedings{Qu2025ExplainableLiDAR,
	title        = {Explainable LiDAR 3D Point Cloud Segmentation and Clustering for Detecting Airplane-Generated Wind Turbulence},
	author       = {Zhan Qu and Shuzhou Yuan and Michael Färber and Marius Brennfleck and Niklas Wartha and Anton Stephan},
	year         = 2025,
	booktitle    = {KDD},
	pages        = {2504--2513},
	location     = {Toronto, Canada},
	url          = {https://arxiv.org/abs/2503.00518},
	abstract     = {Wake vortices—strong, coherent air turbulences created by aircrafts—pose a significant risk to aviation safety and therefore require accurate and reliable detection methods. In this paper, we present an advanced, explainable machine learning method that utilizes Light Detection and Ranging (LiDAR) data for effective wake vortex detection. Our method leverages a dynamic graph CNN (DGCNN) with semantic segmentation to partition a 3D LiDAR point cloud into meaningful segments. Further refinement is achieved through clustering techniques. A novel feature of our research is the use of a perturbation-based explanation technique, which clarifies the model’s decision-making processes for air traffic regulators and controllers, increasing transparency and building trust. Our experimental results, based on measured and simulated LiDAR scans compared against four baseline methods, underscore the effectiveness and reliability of our approach. This combination of semantic segmentation and clustering for real-time wake vortex tracking significantly advances aviation safety measures, ensuring that these are both effective and comprehensible.},
	keywords     = {LiDAR, explainable AI, point cloud segmentation, clustering, aviation safety},
	pdf          = {publications/KIWI_KDD_ADS_2025.pdf},
	biburl       = {https://dblp.org/rec/conf/kdd/QuY0BWS25.bib},
tldr = {We detect aircraft wake turbulence from LiDAR point clouds with explainable ML, providing transparent evidence for aviation safety decisions.},
plain = {We detect dangerous wake turbulence with LiDAR and explain the model’s decision, like an alarm that not only rings but also points to the evidence in the air.}
}

@phdthesis{DBLP:phd/dnb/Farber17,
	title        = {Semantic Search for Novel Information},
	author       = {Michael F{\"{a}}rber},
	year         = 2017,
	isbn         = {978-3-89838-727-9},
	url          = {https://d-nb.info/1137230576},
	biburl       = {https://dblp.org/rec/phd/dnb/Farber17.bib},
	abstract     = {This book presents novel approaches for detecting and extracting information from unstructured text documents that is both relevant and novel. A central contribution of the proposed approaches is the semantic modeling of both previously available information and newly extracted content. This semantic representation yields several advantages: it enables the resolution of linguistic ambiguities, allows precise specification of information needs with respect to relevance and novelty, and facilitates the integration of knowledge graphs. Specifically, the book makes three main scientific contributions. First, it assesses the suitability of existing large-scale knowledge graphs—namely DBpedia, Freebase, OpenCyc, Wikidata, and YAGO—for detecting novel information in text. Second, it introduces an approach for identifying emerging entities that are missing from a knowledge graph within streams of text documents. Third, it proposes a method for extracting novel, relevant, and semantically structured statements from text. The developed approaches support applications such as recommending emerging entities and novel statements, populating knowledge graphs, and assisting users who rely on timely novel information, including journalists and technology scouts.},
	keywords     = {semantic search, novelty detection, knowledge graphs, information extraction, emerging entities},
tldr = {This thesis develops semantic search methods to detect and extract novel information from text streams, supporting monitoring, knowledge graphs, and timely decision-making.},
plain = {This thesis is about finding truly new facts in a flood of text, like building a semantic metal detector that spots novel information worth saving.}
}

@inproceedings{krause2021bootstrapping,
	title        = {Bootstrapping Multilingual Metadata Extraction: A Showcase in Cyrillic},
	author       = {Johan Krause and Igor Shapiro and Tarek Saier and Michael Färber},
	year         = 2021,
	booktitle    = {SDP@NAACL},
	doi          = {10.18653/v1/2021.sdp-1.8},
	url          = {https://www.researchgate.net/publication/352365459},
	abstract     = {Applications based on scholarly data are of ever increasing importance. This results in disadvantages for areas where high-quality data and compatible systems are not available, such as Cyrillic-script regions. This work showcases techniques for bootstrapping multilingual metadata extraction.},
	keywords     = {multilingual metadata extraction, bootstrapping, scholarly data, Cyrillic script, low-resource languages},
	pdf          = {publications/Cyrillic_SDP2021.pdf},
tldr = {We bootstrap multilingual metadata extraction for Cyrillic scholarly data, reducing barriers for non-Latin research communities and digital libraries.},
plain = {We help computers read Cyrillic scholarly metadata, like teaching a librarian to catalog books in another script so they do not stay invisible.}
}

@inproceedings{saier2023cocon,
	title        = {CoCon: A Data Set on Combined Contextualized Research Artifact Use},
	author       = {Tarek Saier and Youxiang Dong and Michael Färber},
	year         = 2023,
	booktitle    = {JCDL},
	url          = {https://github.com/IllDepence/contextgraph},
    abstract  = {In the wake of information overload in academia, methodologies and systems for search, recommendation, and prediction to aid researchers in identifying relevant research are actively studied and developed. Existing work, however, is limited in terms of granularity, focusing only on the level of papers or a single type of artifact, such as data sets. To enable more holistic analyses and systems dealing with academic publications and their content, we propose CoCon, a large scholarly data set reflecting the combined use of research artifacts, contextualized in academic publications' full-text. Our data set comprises 35 k artifacts (data sets, methods, models, and tasks) and 340 k publications. We additionally formalize a link prediction task for "combined research artifact use prediction" and provide code to utilize analyses of and the development of ML applications on our data. All data and code is publicly available at https://github.com/IllDepence/contextgraph.},
     keywords  = {Scholarly Data, Research Artifacts, Knowledge Graphs, Dataset},
    pdf = {publications/CoCon_JCDL2023.pdf},
tldr = {We release CoCon, a dataset capturing combined use of research artifacts in papers, enabling new prediction and recommendation tasks beyond paper-level analysis.},
plain = {CoCon shows which tools, datasets, and methods tend to appear together in papers, like revealing which ingredients often show up in the same scientific recipes.}
}

@inproceedings{yuan2023evaluating,
	title        = {Evaluating Generative Models for Graph-to-Text Generation},
	author       = {Shuzhou Yuan and Michael Färber},
	year         = 2023,
	booktitle    = {RANLP},
	url          = {https://aclanthology.org/anthology-files/pdf/ranlp/2023.ranlp-1.133.pdf},
    abstract  = {Large language models (LLMs) have been widely employed for graph-to-text generation tasks. However, the process of finetuning LLMs requires significant training resources and annotation work. In this paper, we explore the capability of generative models to generate descriptive text from graph data in a zero-shot setting. Specifically, we evaluate GPT-3 and ChatGPT on two graph-to-text datasets and compare their performance with that of finetuned LLM models such as T5 and BART. Our results demonstrate that generative models are capable of generating fluent and coherent text, achieving BLEU scores of 10.57 and 11.08 for the AGENDA and WebNLG datasets, respectively. However, our error analysis reveals that generative models still struggle with understanding the semantic relations between entities, and they also tend to generate text with hallucinations or irrelevant information. As part of the error analysis, we utilize BERT to detect machine-generated text and achieve high macro-F1 scores. We have made the text generated by generative models publicly available.},
    keywords  = {Graph-to-Text Generation, Large Language Models, Zero-Shot Learning, GPT-3, ChatGPT, T5, BART, Text Generation},
    location = {Varna, Bulgaria},
    pdf = {publications/Graph2Text_RANLP2023.pdf},
tldr = {We benchmark GPT-style models for graph-to-text generation in zero-shot settings and analyze when they hallucinate versus fine-tuned baselines.},
plain = {We test whether language models can describe a network diagram faithfully, like asking a narrator to tell a story from a map and checking where it invents details.}
}

@inproceedings{nguyen2022explaining,
	title        = {Explaining Convolutional Neural Networks by Tagging Filters},
	author       = {Anna Nguyen and Daniel Hagenmayer and Tobias Weller and Michael Färber},
	year         = 2022,
	booktitle    = {AIMLAI@CIKM},
	url          = {https://ceur-ws.org/Vol-3318/short21.pdf},
    pdf          = {publications/FilTag_AIMLAI2022.pdf},
	abstract     = {Convolutional neural networks (CNNs) have achieved astonishing performance on various image classification tasks, but it is difficult for humans to understand how a classification comes about. Recent literature proposes methods to explain the classification process to humans, which mostly focus on visualizing feature maps and filter weights and are not very intuitive for non-experts. In this paper, we propose \textsc{FilTag}, an approach to effectively explain CNNs even to non-experts. The idea is that if images of a class frequently activate a convolutional filter, that filter will be tagged with that class. Based on this tagging, individual image classifications can then be intuitively explained using the tags of the filters that the input image activates. Finally, we show that the tags are useful in analyzing classification errors caused by noisy input images and that the tags can be further processed by machines.},
	keywords     = {explainable AI, convolutional neural networks, filter interpretation, model transparency, image classification},
tldr = {We explain CNN decisions by tagging filters with human-readable labels, producing intuitive explanations and supporting error analysis.},
plain = {We explain image classifiers by naming what their hidden filters react to, like labeling the internal sensors so people can understand why a decision happened.}
}

@inproceedings{shao2024equalization,
  title        = {Advanced Equalization in 112 Gb/s Upstream PON Using a Novel Fourier Convolution-based Network},
  author       = {Chen Shao and Elias Giacoumidis and Patrick Matalla and Jialei Li and Shi Li and Sebastian Randel and Andre Richter and Michael Färber and Tobias Käfer},
  year         = {2024},
  booktitle    = {ECOC},
  location     = {Frankfurt, Germany},
  url          = {https://arxiv.org/abs/2405.02609},
  abstract     = {This work presents FConvNet, a Fourier Convolution-based Network for equalization in upstream PON. It outperforms traditional equalizers in bit-error-rate and power efficiency, demonstrating potential for high-speed optical networks.},
  pdf          = {publications/PONs_ECOC2024.pdf},
  keywords     = {Fourier convolution network, equalization, passive optical networks, optical communications, deep learning},
plain = {We design a smarter signal-cleaning model for fiber links, like tuning a radio with a better filter so high-speed internet arrives with fewer glitches.}
}

@inproceedings{faerber2018babelfy,
	title        = {Annotating Domain-Specific Texts with Babelfy: A Case Study},
	author       = {Michael Färber and Kristian Noullet and Boulos El Asmar},
	year         = 2018,
	booktitle    = {EYRE},
	url          = {https://www.researchgate.net/publication/328030249},
    abstract     = {Freely available large knowledge graphs, such as DBpedia, Wikidata, and YAGO, provide comprehensive representations of general knowledge and therefore form a solid basis for text annotation. However, their application to domain-specific text documents requires careful consideration, and reports on real-world use cases of entity linking based on such knowledge graphs remain scarce. In this paper, we describe a practical use case of annotating German-language customer feedback texts using Babelfy as the text annotation service. We conduct a manual evaluation of the resulting annotations and show that approximately 85\% of the annotations are correct. These results indicate that Babelfy as an annotation approach and BabelNet as its underlying knowledge graph constitute a suitable baseline for the development of domain-specific knowledge graphs and customized entity linking methods.},
	keywords     = {entity linking, domain-specific text, Babelfy, semantic annotation, knowledge graphs},
    pdf          = {publications/Babelfy_EYRE2018.pdf},
tldr = {We evaluate entity linking on German customer feedback using Babelfy and show strong annotation accuracy for practical domain text workflows.},
plain = {We test how well a knowledge tool tags messy, specialized text, like putting accurate name tags on customer feedback so it becomes searchable.}
}

@inproceedings{jatowt2023hint,
	title        = {Automatic Hint Generation},
	author       = {Adam Jatowt and Calvin Gehrer and Michael Färber},
	year         = 2023,
	booktitle    = {ICTIR},
	location     = {Taipei, Taiwan},
	doi          = {10.1145/3578337.3605119},
	url          = {https://dl.acm.org/doi/pdf/10.1145/3578337.3605119},
    abstract     = {At times when answers to user questions are readily and easily available (at essentially zero cost), it is important for humans to maintain their knowledge and strong reasoning capabilities. We believe that in many cases providing hints rather than final answers should be sufficient and beneficial for users as it requires thinking and stimulates learning as well as remembering processes. We propose in this paper a novel task of automatic hint generation that supports users in finding the correct answers to their questions without the need of looking the answers up. As the first attempt towards this new task, we design and implement an approach that uses Wikipedia to automatically provide hints for any input question-answer pair. We then evaluate our approach with a user group of 10 persons and demonstrate that the generated hints help users successfully answer more questions than when provided with baseline hints.},
	keywords     = {hint generation, information retrieval, learning support, question answering, user assistance},
    pdf          = {publications/Hint_Generation_ICTIR2023.pdf},
tldr = {We generate hints from Wikipedia for question-answer pairs so learners can reach answers themselves, supporting reasoning without giving solutions away.},
plain = {We generate helpful hints instead of full answers, like giving a nudge in the right direction so people learn and reason rather than being handed the solution.}
}

@inproceedings{elasmar2021aware,
	title        = {AWARE: An Ontology for Situational Awareness of Autonomous Vehicles in Manufacturing},
	author       = {Boulos El Asmar and Syrine Chelly and Michael Färber},
	year         = 2021,
	booktitle    = {CSKG@AAAI},
	url          = {https://usc-isi-i2.github.io/AAAI21workshop/papers/ElAsmar_CSKGsAAAI-21.pdf},
    abstract     = {With the development of autonomous vehicles, recent research has increasingly focused on the semantic representation of robotic proprioceptive and exteroceptive perceptions, that is, the perception of the robot’s own body and of the external world. Such semantic representations can be queried by reasoning systems to enable what may be referred to as machine awareness. While this aligns with the general goal of artificial intelligence to leverage commonsense knowledge, it particularly relates to challenges in knowledge representation and knowledge elicitation. In this work, we present an ontology for representing background knowledge that grounds the cognitive capabilities of autonomous transport robots. Existing ontologies from the domains of robotics and the Internet of Things are integrated into our approach. We further demonstrate the applicability and extensibility of the proposed ontology in the context of autonomous and automated vehicles deployed in an automobile manufacturing plant. Our robotic situational awareness ontology provides a foundation for organizing and controlling robots in future smart factories and illustrates how situational awareness supports the coexistence of smart autonomous agents.},
	keywords     = {ontology, situational awareness, autonomous vehicles, robotics, knowledge representation},
    pdf          = {publications/AWARE-Ontology_CSKG-AAAI2021.pdf},
tldr = {We develop an ontology for situational awareness of autonomous factory vehicles, enabling structured reasoning about perception and safer operation.},
plain = {We build a shared “mental map” for factory robots, like a common language that helps vehicles describe what they sense and choose safe actions.}
}

@inproceedings{zhang2016xlisa,
	title        = {Exploiting Knowledge Bases for Multilingual and Cross-lingual Semantic Annotation and Search},
	author       = {Lei Zhang and Michael Färber and Andreas Thalhammer and Aditya Mogadala and Achim Rettinger},
	year         = 2016,
	booktitle    = {ISWC},
	location     = {Bethlehem, PA, USA},
	url          = {https://www.researchgate.net/publication/311771440},
	abstract     = {The number of entities contained in large knowledge bases has been growing rapidly, enabling new forms of intelligent information access. At the same time, there is an increasing demand for systems that support multilingual and cross-lingual access to information. In this work, we first present X-LiSA, an infrastructure for multilingual and cross-lingual semantic annotation that provides interfaces for annotating unstructured text in multiple languages using knowledge base resources. Building on X-LiSA, we then demonstrate XKnowSearch!, a novel system for multilingual and cross-lingual semantic search that enables keyword-based retrieval over textual data by leveraging semantic annotations.},
	keywords     = {semantic annotation, multilingual search, cross-lingual retrieval, knowledge bases, entity linking},
	pdf          = {publications/Exploiting_ISWC2016.pdf},
tldr = {We build infrastructure for multilingual semantic annotation and demonstrate cross-lingual semantic search that links text to knowledge base entities.},
plain = {We build a system that highlights the same people and places across different languages, like a multilingual marker that lets readers spot and connect information even when texts are written in different languages.}
}

@inproceedings{Qu2025CoDy,
	title        = {CoDy: Counterfactual Explainers for Dynamic Graphs},
	booktitle    = {ICML},
	location     = {Vancouver, Canada},
	author       = {Zhan Qu and Daniel Gomm and Michael Färber},
	year         = 2025,
	url          = {https://arxiv.org/abs/2403.16846},
	abstract     = {Temporal Graph Neural Networks (TGNNs) are widely used to model dynamic systems where relationships and features evolve over time. Although TGNNs demonstrate strong predictive capabilities in these domains, their complex architectures pose significant challenges for explainability. Counterfactual explanation methods provide a promising solution by illustrating how modifications to input graphs can influence model predictions. To address this challenge, we present CoDy—Counterfactual Explainer for Dynamic Graphs—a model-agnostic, instance-level explanation approach that identifies counterfactual subgraphs to interpret TGNN predictions. CoDy employs a search algorithm that combines Monte Carlo Tree Search with heuristic selection policies, efficiently exploring a vast search space of potential explanatory subgraphs by leveraging spatial, temporal, and local event impact information. Extensive experiments against state-of-the-art factual and counterfactual baselines demonstrate CoDy's effectiveness, with improvements of 16% in AUFSC+ over the strongest baseline. Our code is available at: https://github.com/daniel-gomm/CoDy},
	pdf          = {publications/CoDy_ICML2025.pdf},
    keywords     = {Temporal Graph Neural Networks, Dynamic Graphs, Explainable AI, Counterfactual Explanations, Graph Explanations},
tldr = {CoDy generates counterfactual explanations for dynamic graph models, identifying minimal changes that flip predictions and improving interpretability of temporal GNNs.},
plain = {CoDy explains changing network predictions with “what if” examples, like showing the smallest tweak that would flip a decision so humans can follow the logic.}
}

@misc{yuan2025hallucinations,
	title        = {Hallucinations Can Improve Large Language Models in Drug Discovery},
	author       = {Shuzhou Yuan and Michael Färber},
	year         = 2025,
	url          = {https://arxiv.org/abs/2501.13824},
	abstract     = {Hallucinations in large language models (LLMs), plausible but factually inaccurate text, are often viewed as undesirable. However, recent work suggests that such outputs may hold creative potential. In this paper, we investigate whether hallucinations can improve LLMs on molecule property prediction, a key task in early-stage drug discovery. We prompt LLMs to generate natural language descriptions from molecular SMILES strings and incorporate these often hallucinated descriptions into downstream classification tasks. Evaluating seven instruction-tuned LLMs across five datasets, we find that hallucinations significantly improve predictive accuracy for some models. Notably, Falcon3-Mamba-7B outperforms all baselines when hallucinated text is included, while hallucinations generated by GPT-4o consistently yield the greatest gains between models. We further identify and categorize over 18,000 beneficial hallucinations, with structural misdescriptions emerging as the most impactful type, suggesting that hallucinated statements about molecular structure may increase model confidence. Ablation studies show that larger models benefit more from hallucinations, while temperature has a limited effect. Our findings challenge conventional views of hallucination as purely problematic and suggest new directions for leveraging hallucinations as a useful signal in scientific modeling tasks like drug discovery.},
	keywords    = {hallucinations, LLMs, drug discovery},
	pdf         = {publications/Hallucinations_DrugDiscovery.pdf},
tldr = {We show that some hallucinated LLM descriptions of molecules can improve downstream property prediction, suggesting ways to leverage noisy text in drug discovery.},
plain = {We show that even some AI made-up descriptions can still carry useful signals for drug discovery, like rough sketches that are not perfect but still reveal patterns.}
}

@inproceedings{bogdoll2023influence,
	title        = {Impact, Attention, Influence: Early Assessment of Autonomous Driving Datasets},
	author       = {Daniel Bogdoll and Jonas Hendl and Felix Schreyer and Nishanth Gowda and Michael Färber and J. Marius Zöllner},
	year         = 2023,
	booktitle    = {ICCRE},
	url          = {https://arxiv.org/abs/2301.02200},
    abstract  = {Autonomous Driving (AD), the area of robotics with the greatest potential impact on society, has gained a lot of momentum in the last decade. As a result, the number of datasets in AD has increased rapidly. Creators and users of datasets can benefit from a better understanding of developments in the field. While scientometric analysis has been conducted in other fields, it rarely revolves around datasets. Thus, the impact, attention, and influence of datasets on autonomous driving remains a rarely investigated field. In this work, we provide a scientometric analysis for over 200 datasets in AD. We perform a rigorous evaluation of relations between available metadata and citation counts based on linear regression. Subsequently, we propose an Influence Score to assess a dataset already early on without the need for a track-record of citations, which is only available with a certain delay.},
    pdf    = {publications/AD_Datasets_ICCRE2023.pdf},
    keywords = {autonomous driving},
tldr = {We analyze early impact signals of autonomous-driving datasets and propose an influence score to estimate dataset value before citations mature.},
plain = {We estimate early which self-driving datasets will matter later, like spotting which seedlings are likely to become the strongest plants.}
}

@article{DBLP:journals/corr/abs-2509-04982,
  author       = {Julius Neumann and
                  Robert Lange and
                  Yuni Susanti and
                  Michael F{\"{a}}rber},
  title        = {Optimizing Small Transformer-Based Language Models for Multi-Label Sentiment Analysis in Short Texts},
  journal      = {CoRR},
  volume       = {abs/2509.04982},
  year         = {2025},
  url          = {https://doi.org/10.48550/arXiv.2509.04982},
  doi          = {10.48550/ARXIV.2509.04982},
  eprinttype    = {arXiv},
  eprint       = {2509.04982},
  abstract     = {Sentiment classification on short-text datasets poses substantial challenges, including class imbalance, limited training data, and the inherent subjectivity of sentiment labels. These issues are further amplified by the lack of contextual information in short texts, which increases ambiguity and data sparsity and complicates effective model learning. In this paper, we evaluate the effectiveness of small Transformer-based models, specifically BERT and RoBERTa with fewer than one billion parameters, for multi-label sentiment classification in short-text scenarios. We investigate three key factors influencing performance: (1) continued domain-specific pre-training, (2) data augmentation via automatically generated examples, with a focus on generative data augmentation, and (3) architectural variations of the classification head. Our experimental results show that data augmentation consistently improves classification performance, whereas continued pre-training on augmented data can introduce noise and degrade accuracy. In addition, we find that modifications to the classification head provide only marginal performance gains. Overall, our findings offer practical guidance for optimizing BERT-based models under resource constraints and for refining modeling strategies for sentiment classification on short-text datasets.},
  pdf          = {publications/Sentiment_LDD-ECAI2025.pdf},
tldr = {We study how to optimize small transformer models for short-text multi-label sentiment and show when augmentation helps and when it adds harmful noise.},
plain = {We study how small language models can handle short, messy texts well, like making a compact tool do a big job without losing accuracy.}
}

@article{barman2025lpm,
	title        = {Large Physics Models: Towards a Collaborative Approach with Large Language Models and Foundation Models},
	author       = {Kristian G. Barman and Sascha Caron and Emily Sullivan and Henk W. de Regt and Roberto Ruiz de Austri and Mieke Boon and Michael Färber and Stefan Fröse and Faegheh Hasibi and Andreas Ipp and Rukshak Kapoor and Gregor Kasieczka and Daniel Kostić and Michael Krämer and Tobias Golling and Luis G. Lopez and Jesus Marco and Sydney Otten and Pawel Pawlowski and Pietro Vischia and Erik Weber and Christoph Weniger},
	year         = 2025,
	url          = {https://arxiv.org/abs/2501.05382},
	abstract     = {This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs). These models, based on foundation models such as Large Language Models (LLMs) - trained on broad data - are tailored to address the demands of physics research. LPMs can function independently or as part of an integrated framework. This framework can incorporate specialized tools, including symbolic reasoning modules for mathematical manipulations, frameworks to analyse specific experimental and simulated data, and mechanisms for synthesizing theories and scientific literature. We begin by examining whether the physics community should actively develop and refine dedicated models, rather than relying solely on commercial LLMs. We then outline how LPMs can be realized through interdisciplinary collaboration among experts in physics, computer science, and philosophy of science. To integrate these models effectively, we identify three key pillars: Development, Evaluation, and Philosophical Reflection. Development focuses on constructing models capable of processing physics texts, mathematical formulations, and diverse physical data. Evaluation assesses accuracy and reliability by testing and benchmarking. Finally, Philosophical Reflection encompasses the analysis of broader implications of LLMs in physics, including their potential to generate new scientific understanding and what novel collaboration dynamics might arise in research. Inspired by the organizational structure of experimental collaborations in particle physics, we propose a similarly interdisciplinary and collaborative approach to building and refining Large Physics Models. This roadmap provides specific objectives, defines pathways to achieve them, and identifies challenges that must be addressed to realise physics-specific large scale AI models.},
	keywords    = {LLM, physics, foundation models},
	pdf         = {publications/Large_Physics_Models_2025.pdf},
tldr = {We propose a roadmap for physics-specific foundation models, emphasizing collaborative development, rigorous evaluation, and implications for scientific discovery.},
plain = {We propose a roadmap for “Large Physics Models”, like planning a new AI lab assistant that can read physics papers, handle equations, and be tested properly before it is trusted.}
}

@article{SurveyPhotonics2024,
  title        = {Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey},
  author       = {Chen Shao and Elias Giacoumidis and Syed Moktacim Billah and Shi Li and Jialei Li and Prashasti Sahu and Andr{\'{e}} Richter and Michael Färber and Tobias Käfer},
  year         = {2024},
  journal      = {Photonics},
  volume       = {11},
  number       = {7},
  pages        = {613},
  doi          = {10.3390/photonics11070613},
  url          = {https://doi.org/10.3390/photonics11070613},
  abstract     = {This survey reviews the application of machine learning in short-reach optical systems. It categorizes ML techniques into traditional, convolutional, transformer-based, and Fourier-based models, highlighting their potential in tasks like equalization, fault detection, and bandwidth prediction, with a focus on PONs and hardware efficiency.},
  pdf          = {publications/Survey_Photonics2024.pdf},
  keywords     = {Short-Reach Optical Systems, Optical Communications, PON, Equalization, Transformer Models, Fourier Neural Networks},
plain = {This survey is a field guide to AI in optical networks, explaining what works where and why efficiency and reliability matter for critical infrastructure.}
}

@article{faerber2023diversity,
	title        = {Measuring Variety, Balance, and Disparity: An Analysis of Media Coverage of the 2021 German Federal Election},
	author       = {Michael Färber and Jannik Schwade and Adam Jatowt},
	year         = 2023,
	url          = {https://arxiv.org/abs/2308.03531},
	abstract     = {This paper introduces a framework to assess news diversity based on variety, balance, and disparity. Using over 26,000 headlines from the 2021 German election, it applies topic modeling and proposes a novel metric for disparity, contributing to automated media diversity analysis.},
    pdf          = {publications/GoogleNewsDiversity_arxiv2023.pdf},
    keywords = {News Diversity, Recommender Systems, Media Bias},
tldr = {We measure news diversity in German election coverage using variety, balance, and disparity metrics to support transparent analysis of media pluralism.},
plain = {We measure how diverse news coverage is, like checking whether the media menu offers many perspectives or keeps serving the same dish.}
}

@inproceedings{faerber2023worldrep,
	title        = {Theories About World Representations for the Internet of Things},
	author       = {Michael Färber and Yulia Svetashova and Andreas Harth},
	year         = 2023,
	booktitle    = {CARLA},
	url          = {https://conceptresearch.github.io/CARLA/files/carla_2018/F%C3%A4rber.pdf},
    pdf          = {publications/Theories_World_CARLA2018.pdf},
	abstract     = {This paper discusses intersubjectivity, symbol grounding, and dynamic ontologies as foundational components for modeling knowledge in IoT settings. It connects philosophical theories with practical implications in computer science.},
	keywords     = {Internet of Things, world representations, ontologies, symbol grounding, intersubjectivity},
tldr = {We connect theories of meaning and knowledge representation to IoT practice, highlighting challenges of grounding, intersubjectivity, and modeling change over time.},
plain = {We compare how devices and AI systems build pictures of the world, like comparing different mental models to see which ones support shared understanding.}
}

@article{neidiger2024chemkb,
	title        = {Implementation of an Open Chemistry Knowledge Base with a Semantic Wiki},
	author       = {Charlotte Neidiger and Tarek Saier and Kai Kühn and Victor Larignon and Michael Färber and Claudia Bizzarri and Helena Šimek Tosino and Laura Holzhauer and Michael Erdmann and An Nguyen and Dean Harvey and Pierre Tremouilhac and Claudia Kramer and Daniel Hansch and Nicole Jung and Stefan Bräse},
	year         = 2025,
	journal      = {J. Cheminformatics},
	volume       = {17},
	number       = {1},
	pages        = {99},
	doi          = {10.26434/chemrxiv-2024-5dkhm},
	biburl       = {https://dblp.org/rec/journals/jcheminf/NeidigerSKLFBTHENHTKHSA25.bib},
    abstract     = {In this work, we present a concept for an open chemistry knowledge base aimed at integrating chemical research results into a collaboratively usable platform. To this end, we extend Semantic MediaWiki to support the structured collection and summarization of chemical data reported in scientific publications. We implement tools for capturing chemical structures in machine-readable formats and design dedicated data forms and a data model to ensure standardized input and organization of research results. These extensions enable effective data comparison and contextual analysis within an extensible wiki-based environment. The applicability of the platform is demonstrated through a case study on research related to CO$_2$ reduction in homogeneous photocatalytic systems, highlighting its potential to substantially enhance the collaborative collection and comparison of research outcomes. Overall, this work demonstrates how subject-specific knowledge in chemistry can be collaboratively collected and managed in an open database by integrating cheminformatics workflows into an established knowledge management technology, thereby helping to close gaps in the information infrastructure of scientists without relying on commercial systems.},
    keywords     = {chemistry knowledge base, semantic wiki, cheminformatics, linked data, collaborative knowledge management},
	pdf          = {publications/ChemKB_Cheminformatics2025.pdf},
tldr = {We build an open chemistry knowledge base with a semantic wiki and structured forms, enabling collaborative capture and comparison of chemical research results.},
plain = {We build an open chemistry knowledge base like a shared lab notebook on the web, so results can be entered in a structured way and reused by others.}
}

@misc{stein2024quantum,
	title        = {Incorporating Quantum Advantage in Quantum Circuit Generation through Genetic Programming},
	author       = {Christoph Stein and Michael Färber},
	year         = 2024,
	url          = {https://arxiv.org/abs/2501.09682},
	abstract     = {Designing efficient quantum circuits that leverage quantum advantage compared to classical computing has become increasingly critical. Genetic algorithms have shown potential in generating such circuits through artificial evolution. However, integrating quantum advantage into the fitness function of these algorithms remains unexplored. In this paper, we aim to enhance the efficiency of quantum circuit design by proposing two novel approaches for incorporating quantum advantage metrics into the fitness function of genetic algorithms.1 We evaluate our approaches based on the Bernstein-Vazirani Problem and the Unstructured Database Search Problem as test cases. The results demonstrate that our approaches not only improve the convergence speed of the genetic algorithm but also produce circuits comparable to expert-designed solutions. Our findings suggest that automated quantum circuit design using genetic algorithms that incorporate a measure of quantum advantage is a promising approach to accelerating the development of quantum algorithms.},
	keywords     = {quantum computing},
	pdf          = {publications/Quantum_Genetic_Programming_2024.pdf},
tldr = {We use quantum advantage in genetic programming for circuit generation, improving convergence and producing circuits competitive with expert designs on standard tasks.},
plain = {We evolve quantum circuits by trial and reward, like selective breeding for better designs that solve test problems faster.}
}

@incollection{faerber2021meaning,
	title        = {Theories of Meaning for the Internet of Things},
	author       = {Michael Färber and Yulia Svetashova and Andreas Harth},
	year         = 2021,
	booktitle    = {Language, Cognition, and Mind},
	doi          = {10.1007/978-3-030-69823-2_3},
	url          = {https://doi.org/10.1007/978-3-030-69823-2_3},
    abstract  = {In this chapter, we consider the theoretical foundations for representing knowledge in the Internet of Things context. Specifically, we consider (1) the model-theoretic semantics (i.e., extensional semantics), (2) the possible-world semantics (i.e., intensional semantics), (3) the situation semantics, and (4) the cognitive/distributional semantics. Given the peculiarities of the Internet of Things, we pay particular attention to (a) perception (i.e., how to establish a connection to the world), (b) intersubjectivity (i.e., how to align world representations), and (c) the dynamics of world knowledge (i.e., how to model events). We come to the conclusion that each of the semantic theories helps in modeling specific aspects, but does not sufficiently address all three aspects simultaneously.},
    pdf          = {publications/TheoriesOfMeaning_CoI2021.pdf},
    keywords    = {theory, philosophy},
tldr = {We compare semantic theories for representing meaning in IoT and show why grounding, aligned perspectives, and dynamics must be addressed together.},
plain = {We compare ways machines can connect words to the real world.}
}

@inproceedings{faerber2023vocabexpander,
	title        = {Vocab-Expander: A System for Creating Domain-Specific Vocabularies Based on Word Embeddings},
	author       = {Michael Färber and Nicholas Popovic},
	year         = 2023,
	booktitle    = {RANLP},
	doi          = {10.48550/arXiv.2308.03519},
	url          = {https://arxiv.org/abs/2308.03519},
	abstract     = {VOCAB-EXPANDER is an online tool for building domain-specific vocabularies using word embeddings and ConceptNet. It supports tasks in innovation management, education, and interdisciplinary communication through term expansion and graph-based visualization.},
    pdf = {publications/Vocab-X_RANLP2023.pdf},
    keywords     = {word embeddings, demo, text mining, NLP},
tldr = {Vocab-Expander builds domain vocabularies from embeddings and knowledge bases, helping users discover terms for search, innovation, and interdisciplinary work.},
plain = {Vocab-Expander grows a domain vocabulary like expanding a map legend, suggesting useful terms and links so people can search and communicate more easily.}
}

@inproceedings{yuan2024whyheavy,
	title        = {Why Lift So Heavy? Slimming Large Language Models by Cutting Off the Layers},
	author       = {Shuzhou Yuan and Ercong Nie and Bolei Ma and Michael Färber},
	year         = 2024,
	booktitle    = {IJCNN},
	location     = {Rome, Italy},
	url          = {https://arxiv.org/abs/2402.11700},
	abstract     = {Large Language Models (LLMs) exhibit exceptional capabilities across a wide range of natural language processing tasks. However, their immense size poses challenges for storage, training, and inference due to the billions of parameters introduced through deep layer stacking. While methods such as pruning and distillation can reduce model size, they frequently compromise performance. In this work, we systematically investigate the impact of reducing the number of layers in LLMs. Surprisingly, we find that even significantly shallower models maintain comparable or sometimes superior performance, particularly in prompt-based fine-tuning for text classification. In several cases, single-layer models outperform their fully layered counterparts. These results provide important insights for designing more efficient LLMs that mitigate size constraints while preserving performance, opening new avenues for resource-efficient large-scale language modeling.},
	pdf          = {publications/SlimLLM_IJCNN2024.pdf},
	keywords     = {large language models, layer reduction, model compression, efficient NLP},
tldr = {We show that truncating layers can shrink large language models while retaining strong performance on some tasks.},
plain = {We show that some large language models can go on a diet by removing layers without collapsing on certain tasks, pointing to more efficient and greener AI.}
}

@inproceedings{shapiro2022sequence,
	title        = {Sequence Labeling for Citation Field Extraction from Cyrillic Script References},
	author       = {Igor Shapiro and Tarek Saier and Michael Färber},
	year         = 2022,
	booktitle    = {SDU@AAAI},
	url          = {https://ceur-ws.org/Vol-3164/paper9.pdf},
	abstract     = {Extracting structured data from bibliographic references is a crucial task for the creation of scholarly databases. While approaches, tools, and evaluation data sets for this task exist, there is a distinct lack of support for languages other than English and scripts other than the Latin alphabet, excluding a significant portion of scientific literature written in Cyrillic script languages. To address this problem, we introduce a new multilingual and multidisciplinary data set of over 100,000 labeled reference strings covering multiple Cyrillic languages, including over 700 manually labeled references with the remainder generated synthetically. Using random samples of varying size from this data, we train multiple high-performing sequence labeling BERT models and demonstrate the usability of the proposed data set. We further showcase a multilingual BERT model trained on the synthetic data and evaluated on the manually labeled references, achieving an F1 score of 0.93 and significantly outperforming a retrained state-of-the-art model evaluated on the same data.},
	keywords     = {citation field extraction, sequence labeling, multilingual NLP, Cyrillic script, bibliographic data},
    pdf          = {publications/Cyrillic_Citation_Field_Extraction_SDU-AAAI2022.pdf},
tldr = {We create a large multilingual dataset of Cyrillic references and train accurate sequence labelers for citation-field extraction beyond Latin scripts.},
plain = {We teach models to parse Cyrillic references, like sorting foreign-language mail into the right slots so citation data can be extracted reliably.}
}