publications

2026

1. PoeTone: A Framework for Constrained Generation of Structured Chinese Songci with LLMs

   Zhan Qu, Shuzhou Yuan, and Michael Färber

   In AAAI, Singapore, Singapore, 2026

   Abs PDF

   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.

2025

1. SQuAI: Scientific Question-Answering with Multi-Agent Retrieval-Augmented Generation

   Ines Besrour, Jingbo He, Tobias Schreieder, and 1 more author

   In CIKM, Seoul, South Korea, 2025

   Abs PDF

   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.

2. Real-E: A Foundation Benchmark for Advancing Robust and Generalizable Electricity Forecasting

   Chen Shao, Yue Wang, Zhenyi Zhu, and 5 more authors

   In CIKM, Seoul, South Korea, 2025

   Abs PDF

   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.

3. Hateful Person or Hateful Model? Investigating the Role of Personas in Hate Speech Detection by Large Language Models

   Shuzhou Yuan, Ercong Nie, Mario Tawfelis, and 3 more authors

   In PALS@EMNLP, 2025

   Abs PDF

   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.

4. ComplexTempQA: A 100m Dataset for Complex Temporal Question Answering

   Raphael Gruber, Abdelrahman Abdallah, Michael Färber, and 1 more author

   In EMNLP, Suzhou, China, 2025

   Abs PDF

   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.

5. Extractive Fact Decomposition for Interpretable Natural Language Inference in One Forward Pass

   Nicholas Popovic, and Michael Färber

   In EMNLP, Suzhou, China, 2025

   Abs PDF

   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.

6. Graph-Guided Textual Explanation Generation Framework

   Shuzhou Yuan, Jingyi Sun, Ran Zhang, and 4 more authors

   In EMNLP, 2025

   Abs PDF

   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.

7. In-Context Learning for Information Extraction using Fully Synthetic Demonstrations

   Nicholas Popovic, Ashish Kangen, Tim Schopf, and 1 more author

   In XLLM@ACL, Vienna, Austria, 2025

   Abs PDF

   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.

8. Natural Language Inference Fine-tuning for Scientific Hallucination Detection

   In SDP@ACL, Vienna, Austria, 2025

   Abs PDF

   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.

9. Paths to Causality: Finding Informative Subgraphs within Knowledge Graphs for Knowledge-Based Causal Discovery

   Yuni Susanti, and Michael Färber

   In KDD, 2025

   Abs DOI PDF

   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.

10. Revisiting Projection-based Data Transfer for Cross-Lingual Named Entity Recognition in Low-Resource Languages

    Andrei Politov, Oleh Shkalikov, René Jäkel, and 1 more author

    In NoDaLiDa/Baltic-HLT, Tallinn, Estonia, 2025

    Abs PDF

    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.

11. Predicting Company ESG Ratings from News Articles Using Multivariate Time-series Analysis

    Tanja Aue, Adam Jatowt, and Michael Färber

    In TempWeb@WWW, Sydney, Australia, 2025

    Abs PDF

    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.

12. SimplifyMyText: An LLM-Based System for Inclusive Plain Language Text Simplification

    Michael Färber, Parisa Aghdam, Kyuri Im, and 2 more authors

    In ECIR, Lucca,Italy, 2025

    Abs PDF

    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.

13. Explainable LiDAR 3D Point Cloud Segmentation and Clustering for Detecting Airplane-Generated Wind Turbulence

    Zhan Qu, Shuzhou Yuan, Michael Färber, and 3 more authors

    In KDD, Toronto, Canada, 2025

    Abs PDF

    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.

14. CoDy: Counterfactual Explainers for Dynamic Graphs

    Zhan Qu, Daniel Gomm, and Michael Färber

    In ICML, Vancouver, Canada, 2025

    Abs PDF

    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

15. Hallucinations Can Improve Large Language Models in Drug Discovery

    Shuzhou Yuan, and Michael Färber

    2025

    Abs PDF

    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.

16. Large Physics Models: Towards a Collaborative Approach with Large Language Models and Foundation Models

    Kristian G. Barman, Sascha Caron, Emily Sullivan, and 19 more authors

    2025

    Abs PDF

    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.

2024

1. Assessing Privacy Policies with AI: Ethical, Legal, and Technical Challenges

   Irem Aydin, Hermann Diebel-Fischer, Vincent Freiberger, and 5 more authors

   In AISyS, 2024

   Abs PDF

   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.

2. ML approaches for OTDR diagnoses in passive optical networks - event detection and classification: ways for ODN branch assignment

   Michael Straub, Johannes Reber, Tarek Saier, and 7 more authors

   J. Opt. Commun. Netw., 2024

   Abs DOI PDF

   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%.

3. Benefits of international collaboration in computer science: a case study of China, the European Union, and the United States

   Alberto Gómez-Espés, Michael Färber, and Adam Jatowt

   Scientometrics, 2024

   Abs DOI PDF

   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.

4. Future Timelines: Extraction and Visualization of Future-related Content From News Articles

   Juwal Regev, Adam Jatowt, and Michael Färber

   In WSDM, 2024

   Abs DOI PDF

   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.

5. Embedded Named Entity Recognition using Probing Classifiers

   Nicholas Popovic, and Michael Färber

   In EMNLP, Miami, FL, USA, 2024

   Abs PDF

   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.

6. The Effects of Hallucinations in Synthetic Training Data for Relation Extraction

   Steven Rogulsky, Nicholas Popovic, and Michael Färber

   In KBC-LM@ISWC, Baltimore, USA, 2024

   Abs PDF

   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.

7. Knowledge Graph Structure as Prompt: Improving Small Language Models Capabilities for Knowledge-Based Causal Discovery

   Yuni Susanti, and Michael Färber

   In ISWC, Baltimore, MD, USA, 2024

   Abs DOI PDF

   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.

8. AutoRDF2GML: Facilitating RDF Integration in Graph Machine Learning

   Michael Färber, David Lamprecht, and Yuni Susanti

   In ISWC, Baltimore, MD, USA, 2024

   Abs DOI PDF

   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.

9. GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network

   Shuzhou Yuan, Ercong Nie, Michael Färber, and 2 more authors

   In ACL, Bangkok, Thailand, 2024

   Abs DOI PDF

   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.

10. GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanism

    Shuzhou Yuan, and Michael Färber

    In NAACL, Mexico City, Mexico, 2024

    Abs DOI PDF

    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

11. KITspotlight: A System for Spotlighting Researchers in the Media

    Michael Färber, Benjamin Zagoruiko, and Markus Wambach

    In ICWE, Tampere, Finland, 2024

    Abs DOI PDF

    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.

12. ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks

    Bolei Ma, Ercong Nie, Shuzhou Yuan, and 4 more authors

    In EACL, Malta, 2024

    Abs PDF

    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

13. HyperPIE: Hyperparameter Information Extraction from Scientific Publications

    Tarek Saier, Mayumi Ohta, Takuto Asakura, and 1 more author

    In ECIR, Glasgow, UK, 2024

    Abs DOI PDF

    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 \urlhttps://github.com/IllDepence/hyperpie.

14. A Novel Machine Learning-based Equalizer for a Downstream 100G PAM-4 PON

    Chen Shao, Elias G. Giacoumidis, Shi Li, and 4 more authors

    In OFC, San Diego, CA, USA, 2024

    Abs PDF

    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.

15. Advanced Equalization in 112 Gb/s Upstream PON Using a Novel Fourier Convolution-based Network

    Chen Shao, Elias Giacoumidis, Patrick Matalla, and 6 more authors

    In ECOC, Frankfurt, Germany, 2024

    Abs PDF

    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.

16. Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey

    Chen Shao, Elias Giacoumidis, Syed Moktacim Billah, and 6 more authors

    Photonics, 2024

    Abs DOI PDF

    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.

17. Implementation of an Open Chemistry Knowledge Base with a Semantic Wiki

    Charlotte Neidiger, Tarek Saier, Kai Kühn, and 13 more authors

    J. Cheminformatics, 2024

    Abs DOI PDF

    We present a concept for an open chemistry knowledge base that integrates research results into a collaboratively usable platform. To achieve this, we enhanced Semantic MediaWiki (SMW) with tools for capturing chemical structures in machine-readable formats and with data forms and models for standardized input and organization. These extensions enable effective comparison and contextual analysis of research within an expandable Wiki environment. The approach was demonstrated in the domain of CO2 reduction in homogeneous photocatalytic systems. Our work shows how subject-specific knowledge in chemistry can be collaboratively collected and managed by integrating cheminformatic tools into SMW, thereby providing a systematic, open alternative to commercial systems.

18. Incorporating Quantum Advantage in Quantum Circuit Generation through Genetic Programming

    Christoph Stein, and Michael Färber

    2024

    Abs PDF

    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.

19. Why Lift So Heavy? Slimming Large Language Models by Cutting Off the Layers

    Shuzhou Yuan, Ercong Nie, Bolei Ma, and 1 more author

    In IJCNN, Rome, Italy, 2024

    Abs PDF

    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.

2023

1. Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph

   Michael Färber, and David Lamprecht

   In ISWC, Athens, Greece, 2023

   Abs PDF

   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.

2. SemOpenAlex: The Scientific Landscape in 26 Billion RDF Triples

   Michael Färber, David Lamprecht, Johan Krause, and 2 more authors

   In ISWC, 2023

   Abs PDF

   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.

3. Biases in Scholarly Recommender Systems: Impact, Prevalence, and Mitigation

   Michael Färber, Melissa Coutinho, and Shuzhou Yuan

   Scientometrics, 2023

   Abs DOI PDF

   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.

4. AI-Based OTDR Event Detection, Classification and Assignment to ODN Branches in Passive Optical Networks

   Michael Straub, T. Saier, J. Reber, and 6 more authors

   Journal of Optical Communications and Networking, 2023

   Abs DOI PDF

   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.

5. Analyzing the Impact of Companies on AI Research Based on Publications

   Michael Färber, and Lazaros Tampakis

   Scientometrics, 2023

   Abs DOI PDF

   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.

6. A Full-Fledged Framework for Combining Entity Linking Systems and Components

   Kristian Noullet, Ayoub Ourgani, and Michael Färber

   In K-CAP, 2023

   Abs DOI PDF

   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).

7. A Full-fledged Commit Message Quality Checker Based on Machine Learning

   David Faragó, Michael Färber, and Christian Petrov

   In COMPSAC, Torino, Italy, 2023

   Abs DOI PDF

   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.

8. Ablesbarkeitsmesser: A System for Assessing the Readability of German Text

   Florian Pickelmann, Michael Färber, and Adam Jatowt

   In ECIR, Dublin, Ireland, 2023

   Abs DOI PDF

   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.

9. CoCon: A Data Set on Combined Contextualized Research Artifact Use

   Tarek Saier, Youxiang Dong, and Michael Färber

   In JCDL, 2023

   Abs PDF

   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.

10. Evaluating Generative Models for Graph-to-Text Generation

    Shuzhou Yuan, and Michael Färber

    In RANLP, Varna, Bulgaria, 2023

    Abs PDF

    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.

11. Automatic Hint Generation

    Adam Jatowt, Calvin Gehrer, and Michael Färber

    In ICTIR, Taipei, Taiwan, 2023

    Abs DOI PDF

    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.

12. Impact, Attention, Influence: Early Assessment of Autonomous Driving Datasets

    Daniel Bogdoll, Jonas Hendl, Felix Schreyer, and 3 more authors

    In ICCRE, 2023

    Abs PDF

    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.

13. Measuring Variety, Balance, and Disparity: An Analysis of Media Coverage of the 2021 German Federal Election

    Michael Färber, Jannik Schwade, and Adam Jatowt

    2023

    Abs PDF

    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.

14. Theories About World Representations for the Internet of Things

    Michael Färber, Yulia Svetashova, and Andreas Harth

    In CARLA, 2023

    Abs PDF

    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.

15. Vocab-Expander: A System for Creating Domain-Specific Vocabularies Based on Word Embeddings

    Michael Färber, and Nicholas Popovic

    In RANLP, 2023

    Abs DOI PDF

    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.

2022

1. The Microsoft Academic Knowledge Graph enhanced: Author name disambiguation, publication classification, and embeddings

   Michael Färber, and Lin Ao

   Quant. Science Studies, 2022

   Abs DOI PDF

   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.

2. The Green AI Ontology: An Ontology for Modeling the Energy Consumption of AI Models

   Michael Färber, and David Lamprecht

   In ISWC, Virtual Conference, Hangzhou, China, 2022

   Abs PDF

   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.

3. A Blocking-Based Approach to Enhance Large-Scale Reference Linking

   Tarek Saier, Meng Luan, and Michael Färber

   In LITE@JCDL, 2022

   Abs PDF

   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).

4. How Does Author Affiliation Affect Preprint Citation Count? Analyzing Citation Bias at the Institution and Country Level

   Chifumi Nishioka, Michael Färber, and Tarek Saier

   In JCDL, Cologne, Germany, 2022

   Abs DOI PDF

   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.

5. Few-Shot Document-Level Relation Extraction

   Nicholas Popovic, and Michael Färber

   In NAACL, Seattle, WA, USA, 2022

   Abs DOI PDF

   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.

6. AIFB-WebScience at SemEval-2022 Task 12: Relation Extraction First - Using Relation Extraction to Identify Entities

   Nicholas Popovic, Walter Laurito, and Michael Färber

   In SemEval@NAACL, 2022

   Abs DOI PDF

   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.

7. Which Publications’ Metadata Are in Which Bibliographic Databases? A System for Exploration

   Michael Färber, Christoph Braun, Nicholas Popovic, and 2 more authors

   In BIR@ECIR, Stavanger, Norway, 2022

   Abs PDF

   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.

8. Safety Aware Reinforcement Learning by Identifying Comprehensible Constraints in Expert Demonstrations

   Leopold Müller, Lars Böcking, and Michael Färber

   In SafeAI@AAAI, 2022

   Abs PDF

   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.

9. When to Use Which Neural Network? Finding the Right Neural Network Architecture for a Research Problem

   Michael Färber, and Nicolas Weber

   In SDU@AAAI, 2022

   Abs PDF

   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.

10. Explaining Convolutional Neural Networks by Tagging Filters

    Anna Nguyen, Daniel Hagenmayer, Tobias Weller, and 1 more author

    In AIMLAI@CIKM, 2022

    Abs PDF

    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 \textscFilTag, 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.

11. Sequence Labeling for Citation Field Extraction from Cyrillic Script References

    Igor Shapiro, Tarek Saier, and Michael Färber

    In SDU@AAAI, 2022

    Abs PDF

    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.

2021

1. The Data Set Knowledge Graph: Creating a Linked Open Data Source for Data Sets

   Michael Färber, and David Lamprecht

   Quant. Science Studies, 2021

   Abs PDF

   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.

2. Cross-Lingual Citations in English Papers: A Large-Scale Analysis of Prevalence, Usage, and Impact

   Tarek Saier, Michael Färber, and Tornike Tsereteli

   International Journal on Digital Libraries, 2021

   Abs PDF

   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.

3. Improving Question Answering for Event-focused Questions in Temporal Collections of News Articles

   Jiexin Wang, Adam Jatowt, Michael Färber, and 1 more author

   Information Retrieval Journal, 2021

   Abs PDF

   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.

4. Towards Full-Fledged Argument Search: A Framework for Extracting and Clustering Arguments from Unstructured Text

   Michael Färber, and Anna Steyer

   CoRR, 2021

   Abs PDF

   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.

5. Are Investors Biased Against Women? Analyzing How Gender Affects Startup Funding in Europe

   Michael Färber, and Alexander Klein

   CoRR, 2021

   Abs PDF

   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.

6. Recommending Datasets for Scientific Problem Descriptions

   Michael Färber, and Ann-Kathrin Leisinger

   In CIKM, Virtual Event, Queensland, Australia, 2021

   Abs DOI PDF

   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.

7. DataHunter: A System for Finding Datasets Based on Scientific Problem Descriptions

   Michael Färber, and Ann-Kathrin Leisinger

   In RecSys, Amsterdam, The Netherlands, 2021

   Abs DOI PDF

   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.

8. Quantifying Explanations of Neural Networks in E-Commerce Based on LRP

   Anna Nguyen, Franz Krause, Daniel Hagenmayer, and 1 more author

   In ECML-PKDD, Bilbao, Spain, 2021

   Abs DOI PDF

   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.

9. CLiT: Combining Linking Techniques for Everyone

   Kristian Noullet, Samuel Printz, and Michael Färber

   In ESWC, 2021

   Abs DOI

10. Media Bias Everywhere? A Vision for Dealing with the Manipulation of Public Opinion

    Michael Färber, and Frederic Bartscherer

    In BIAS, Lucca, Italy, 2021

    Abs DOI

11. C-Rex: A Comprehensive System for Recommending In-Text Citations with Explanations

    Michael Färber, Vinzenz Zinecker, Isabela Bragaglia Cartus, and 2 more authors

    In Sci-K@WWW, Virtual Event, Ljubljana, Slovenia, 2021

    Abs DOI

12. Exploding TV Sets and Disappointing Laptops: Suggesting Interesting Content in News Archives Based on Surprise Estimation

    Adam Jatowt, I-Chen Hung, Michael Färber, and 2 more authors

    In ECIR, 2021

    Abs DOI

13. Right for the Right Reasons: Making Image Classification Intuitively Explainable

    Anna Nguyen, Adrian Oberföll, and Michael Färber

    In ECIR, Virtual Event, 2021

    Abs DOI

14. Identifying Used Methods and Datasets in Scientific Publications

    Michael Färber, Alexander Albers, and Felix Schüber

    In SDU@AAAI, 2021

    Abs

15. Bootstrapping Multilingual Metadata Extraction: A Showcase in Cyrillic

    Johan Krause, Igor Shapiro, Tarek Saier, and 1 more author

    In SDP@NAACL, 2021

    Abs DOI

    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.

16. AWARE: An Ontology for Situational Awareness of Autonomous Vehicles in Manufacturing

    Boulos El Asmar, Syrine Chelly, and Michael Färber

    In CSKG@AAAI, 2021

    Abs

    AWARE is an ontology designed to represent situational awareness for autonomous transport robots in manufacturing. It integrates perceptions, decision logic, and environment context to support autonomy and reasoning.

17. Theories of Meaning for the Internet of Things

    Michael Färber, Yulia Svetashova, and Andreas Harth

    In Language, Cognition, and Mind, 2021

    Abs DOI PDF

    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.

2020

1. Citation recommendation: approaches and datasets

   Michael Färber, and Adam Jatowt

   Int. J. Digit. Libr., 2020

   Abs DOI PDF

2. unarXive: a large scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata

   Tarek Saier, and Michael Färber

   Scientometrics, 2020

   Abs DOI PDF

3. Analyzing the GitHub Repositories of Research Papers

   Michael Färber

   In JCDL, Virtual Event, China, 2020

   Abs DOI

4. HybridCite: A Hybrid Model for Context-Aware Citation Recommendation

   Michael Färber, and Ashwath Sampath

   In JCDL, Virtual Event, China, 2020

   Abs DOI

5. Who’s Behind That Website? Classifying Websites by the Degree of Commercial Intent

   Michael Färber, Benjamin Scheer, and Frederic Bartscherer

   In ICWE, Helsinki, Finland, 2020

   Abs DOI

6. Annotating and Analyzing Biased Sentences in News Articles using Crowdsourcing

   Sora Lim, Adam Jatowt, Michael Färber, and 1 more author

   In LREC, Marseille, France, 2020

   Abs

7. KORE 50^DYWC: An Evaluation Data Set for Entity Linking Based on DBpedia, YAGO, Wikidata, and Crunchbase

   Kristian Noullet, Rico Mix, and Michael Färber

   In LREC, Marseille, France, 2020

   Abs

8. Answering Event-Related Questions over Long-Term News Article Archives

   Jiexin Wang, Adam Jatowt, Michael Färber, and 1 more author

   In ECIR, Lisbon, Portugal, 2020

   Abs DOI

9. Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation

   Tarek Saier, and Michael Färber

   In ECIR, Lisbon, Portugal, 2020

   Abs DOI

10. Neural Citation Recommendation: A Reproducibility Study

    Michael Färber, Timo Klein, and Joan Sigloch

    In BIR@ECIR, Lisbon, Portugal, 2020

    Abs

2019

1. Linked Crunchbase: A Linked Data API and RDF Data Set About Innovative Companies

   Michael Färber

   CoRR, 2019

   Abs PDF

2. The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data

   Michael Färber

   In ISWC, Auckland, New Zealand, 2019

   Abs DOI PDF

3. Identifying Twitter Bots Using a Convolutional Neural Network

   Michael Färber, Agon Qurdina, and Lule Ahmedi

   In CLEF, Lugano, Switzerland, 2019

   Abs PDF

4. Determining How Citations Are Used in Citation Contexts

   Michael Färber, and Ashwath Sampath

   In TPDL, Oslo, Norway, 2019

   Abs DOI PDF

5. Evaluating the Availability of Open Citation Data

   Chifumi Nishioka, and Michael Färber

   In BIRNDL@SIGIR, Paris, France, 2019

   Abs PDF

6. Relational schemata for distributed SPARQL query processing

   Victor Anthony Arrascue Ayala, Polina Koleva, Anas Alzogbi, and 6 more authors

   In SBD@SIGMOD, Amsterdam, The Netherlands, 2019

   Abs DOI PDF

7. ScholarSight: Visualizing Temporal Trends of Scientific Concepts

   Michael Färber, Chifumi Nishioka, and Adam Jatowt

   In JCDL, Champaign, IL, USA, 2019

   Abs DOI PDF

8. Team Peter Brinkmann at SemEval-2019 Task 4: Detecting Biased News Articles Using Convolutional Neural Networks

   Michael Färber, Agon Qurdina, and Lule Ahmedi

   In SemEval@NAACL-HLT 2019, Minneapolis, MN, USA, 2019

   Abs DOI PDF

9. Bibliometric-Enhanced arXiv: A Data Set for Paper-Based and Citation-Based Tasks

   Tarek Saier, and Michael Färber

   In BIR@ECIR, Cologne, Germany, 2019

   Abs PDF

10. Finding Temporal Trends of Scientific Concepts

    Michael Färber, and Adam Jatowt

    In BIR@ECIR, Cologne, Germany, 2019

    Abs PDF

11. PaperHunter: A System for Exploring Papers and Citation Contexts

    Michael Färber, Ashwath Sampath, and Adam Jatowt

    In ECIR, Cologne, Germany, 2019

    Abs DOI PDF

2018

1. A Linked Data wrapper for CrunchBase

   Michael Färber, Carsten Menne, and Andreas Harth

   Semantic Web, 2018

   Abs DOI PDF

2. Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO

   Michael Färber, Frederic Bartscherer, Carsten Menne, and 1 more author

   Semantic Web, 2018

   Abs DOI PDF

3. Which Knowledge Graph Is Best for Me?

   Michael Färber, and Achim Rettinger

   CoRR, 2018

   Abs PDF

4. A High-Quality Gold Standard for Citation-based Tasks

   Michael Färber, Alexander Thiemann, and Adam Jatowt

   In LREC, Miyazaki, Japan, 2018

   Abs PDF

5. PRoST: Distributed Execution of SPARQL Queries Using Mixed Partitioning Strategies

   Matteo Cossu, Michael Färber, and Georg Lausen

   In EDBT, Vienna, Austria, 2018

   Abs DOI PDF

6. CITEWERTs: A System Combining Cite-Worthiness with Citation Recommendation

   Michael Färber, Alexander Thiemann, and Adam Jatowt

   In ECIR, Grenoble, France, 2018

   Abs DOI PDF

7. To Cite, or Not to Cite? Detecting Citation Contexts in Text

   Michael Färber, Alexander Thiemann, and Adam Jatowt

   In ECIR, Grenoble, France, 2018

   Abs DOI PDF

8. Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO

   Michael Färber, Frederic Bartscherer, Carsten Menne, and 1 more author

   Semantic Web, 2018

   DOI
9. Annotating Domain-Specific Texts with Babelfy: A Case Study

   Michael Färber, Kristian Noullet, and Boulos El Asmar

   In EYRE, 2018

   Abs

   This case study evaluates the performance of Babelfy for annotating domain-specific German texts. Results show that Babelfy achieves about 85% annotation accuracy, suggesting its viability for industrial applications.

2017

1. The xLiMe system: Cross-lingual and cross-modal semantic annotation, search and recommendation over live-TV, news and social media streams

   Lei Zhang, Andreas Thalhammer, Achim Rettinger, and 3 more authors

   Web Semantics, 2017

   Abs DOI PDF

2. Semantic search for novel information

   Michael Färber

   2017

   Abs

2016

1. XKnowSearch! Exploiting Knowledge Bases for Entity-based Cross-lingual Information Retrieval

   Lei Zhang, Michael Färber, and Achim Rettinger

   In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, Indianapolis, Indiana, USA, 2016

   Abs DOI PDF

   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.

2. Exploiting Knowledge Bases for Multilingual and Cross-lingual Semantic Annotation and Search

   Lei Zhang, Michael Färber, Andreas Thalhammer, and 2 more authors

   In ISWC, Bethlehem, PA, USA, 2016

   Abs

   This paper presents X-LiSA, an infrastructure for multilingual and cross-lingual semantic annotation, and XKnowSearch!, a system for entity-based semantic search. These tools utilize large knowledge bases to improve cross-lingual access to information and semantic search through disambiguation and translation of keyword queries.

2015

1. Using a semantic wiki for technology forecast and technology monitoring

   Michael Färber

   Program, 2015

   Abs DOI PDF

2014

1. Kuphi - an Investigation Tool for Searching for and via Semantic Relations

   Michael Färber, Lei Zhang, and Achim Rettinger

   In ESWC, Anissaras, Crete, Greece, 2014

   Abs DOI PDF

2. xLiD-Lexica: Cross-lingual Linked Data Lexica

   Lei Zhang, Michael Färber, and Achim Rettinger

   In LREC, Reykjavik, Iceland, 2014

   Abs PDF

3. Exploiting Semantic Annotations for Entity-based Information Retrieval

   Lei Zhang, Michael Färber, Thanh Tran, and 1 more author

   In ISWC, Riva del Garda, Italy, 2014

   Abs PDF

2013

1. A Comparative Evaluation of Cross-Lingual Text Annotation Techniques

   Lei Zhang, Achim Rettinger, Michael Färber, and 1 more author

   In CLEF, 2013

   Abs DOI PDF

2. Ontology-Supported Document Ranking for Novelty Search

   Michael Färber

   In ESWC, Montpellier, France, 2013

   Abs DOI PDF

3. A semantic wiki for novelty search on documents

   Michael Färber, and Achim Rettinger

   In BIR, Delft, The Netherlands, 2013

   Abs PDF