arXiv subjects

Stefan Dietze

Publications and source records attributed to Stefan Dietze.

At least 19 recordsLinked to original sources

arXiv2026

The CLEF-2026 CheckThat! Lab: Advancing Multilingual Fact-Checking

The CheckThat! lab aims to advance the development of innovative technologies combating disinformation and manipulation efforts in online communication across a multitude of languages and platforms. While in early editions the focus has been on core tasks of the verification pipeline (check-worthiness, evidence retrieval, and verification), in the past three editions, the lab added additional tasks linked to the verification process. In this year's edition, the verification pipeline is at the center again with the following tasks: Task 1 on source retrieval for scientific web claims (a follow-up of the 2025 edition), Task 2 on fact-checking numerical and temporal claims, which adds a reasoning component to the 2025 edition, and Task 3, which expands the verification pipeline with generation of full-fact-checking articles. These tasks represent challenging classification and retrieval problems as well as generation challenges at the document and span level, including multilingual settings.

cs.CL↗

arXiv2025

GSAP-ERE: Fine-Grained Scholarly Entity and Relation Extraction Focused on Machine Learning

Research in Machine Learning (ML) and AI evolves rapidly. Information Extraction (IE) from scientific publications enables to identify information about research concepts and resources on a large scale and therefore is a pathway to improve understanding and reproducibility of ML-related research. To extract and connect fine-grained information in ML-related research, e.g. method training and data usage, we introduce GSAP-ERE. It is a manually curated fine-grained dataset with 10 entity types and 18 semantically categorized relation types, containing mentions of 63K entities and 35K relations from the full text of 100 ML publications. We show that our dataset enables fine-tuned models to automatically extract information relevant for downstream tasks ranging from knowledge graph (KG) construction, to monitoring the computational reproducibility of AI research at scale. Additionally, we use our dataset as a test suite to explore prompting strategies for IE using Large Language Models (LLM). We observe that the performance of state-of-the-art LLM prompting methods is largely outperformed by our best fine-tuned baseline model (NER: 80.6%, RE: 54.0% for the fine-tuned model vs. NER: 44.4%, RE: 10.1% for the LLM). This disparity of performance between supervised models and unsupervised usage of LLMs suggests datasets like GSAP-ERE are needed to advance research in the domain of scholarly information extraction.

cs.CL↗

arXiv2025

NFDI4DS Shared Tasks for Scholarly Document Processing

Shared tasks are powerful tools for advancing research through community-based standardised evaluation. As such, they play a key role in promoting findable, accessible, interoperable, and reusable (FAIR), as well as transparent and reproducible research practices. This paper presents an updated overview of twelve shared tasks developed and hosted under the German National Research Data Infrastructure for Data Science and Artificial Intelligence (NFDI4DS) consortium, covering a diverse set of challenges in scholarly document processing. Hosted at leading venues, the tasks foster methodological innovations and contribute open-access datasets, models, and tools for the broader research community, which are integrated into the consortium's research data infrastructure.

cs.CL↗

arXiv2025

A Research Vision for Web Search on Emerging Topics

We regularly encounter information on novel, emerging topics for which the body of knowledge is still evolving, which can be linked, for instance, to current events. A primary way to learn more about such topics is through web search. However, information on emerging topics is sparse and evolves dynamically as knowledge grows, making it uncertain and variable in quality and trustworthiness and prone to deliberate or accidental manipulation, misinformation, and bias. In this paper, we outline a research vision towards search systems and interfaces that support effective knowledge acquisition, awareness of the dynamic nature of topics, and responsible opinion formation among people searching the web for information on emerging topics. To realize this vision, we propose three overarching research questions, aimed at understanding the status quo, determining requirements of systems aligned with our vision, and building these systems. For each of the three questions, we highlight relevant literature, including pointers on how they could be addressed. Lastly, we discuss the challenges that will potentially arise in pursuing the proposed vision.

cs.IR↗

arXiv2025

Research Knowledge Graphs in NFDI4DataScience: Key Activities, Achievements, and Future Directions

As research in Artificial Intelligence and Data Science continues to grow in volume and complexity, it becomes increasingly difficult to ensure transparency, reproducibility, and discoverability. To address these challenges, as research artifacts should be understandable and usable by machines, the NFDI4DataScience consortium is developing and providing Research Knowledge Graphs (RKGs). Building upon earlier works, this paper presents recent progress in creating semantically rich RKGs using standardized ontologies, shared vocabularies, and automated Information Extraction techniques. Key achievements include the development of the NFDI4DS ontology, metadata standards, tools, and services designed to support the FAIR principles, as well as community-led projects and various implementations of RKGs. Together, these efforts aim to capture and connect the complex relationships between datasets, models, software, and scientific publications.

cs.IR↗

arXiv2025

Research Knowledge Graphs: the Shifting Paradigm of Scholarly Information Representation

Sharing and reusing research artifacts, such as datasets, publications, or methods is a fundamental part of scientific activity, where heterogeneity of resources and metadata and the common practice of capturing information in unstructured publications pose crucial challenges. Reproducibility of research and finding state-of-the-art methods or data have become increasingly challenging. In this context, the concept of Research Knowledge Graphs (RKGs) has emerged, aiming at providing an easy to use and machine-actionable representation of research artifacts and their relations. That is facilitated through the use of established principles for data representation, the consistent adoption of globally unique persistent identifiers and the reuse and linking of vocabularies and data. This paper provides the first conceptualisation of the RKG vision, a categorisation of in-use RKGs together with a description of RKG building blocks and principles. We also survey real-world RKG implementations differing with respect to scale, schema, data, used vocabulary, and reliability of the contained data. We also characterise different RKG construction methodologies and provide a forward-looking perspective on the diverse applications, opportunities, and challenges associated with the RKG vision.

cs.IR↗

arXiv2025

Limited Generalizability in Argument Mining: State-Of-The-Art Models Learn Datasets, Not Arguments

Identifying arguments is a necessary prerequisite for various tasks in automated discourse analysis, particularly within contexts such as political debates, online discussions, and scientific reasoning. In addition to theoretical advances in understanding the constitution of arguments, a significant body of research has emerged around practical argument mining, supported by a growing number of publicly available datasets. On these benchmarks, BERT-like transformers have consistently performed best, reinforcing the belief that such models are broadly applicable across diverse contexts of debate. This study offers the first large-scale re-evaluation of such state-of-the-art models, with a specific focus on their ability to generalize in identifying arguments. We evaluate four transformers, three standard and one enhanced with contrastive pre-training for better generalization, on 17 English sentence-level datasets as most relevant to the task. Our findings show that, to varying degrees, these models tend to rely on lexical shortcuts tied to content words, suggesting that apparent progress may often be driven by dataset-specific cues rather than true task alignment. While the models achieve strong results on familiar benchmarks, their performance drops markedly when applied to unseen datasets. Nonetheless, incorporating both task-specific pre-training and joint benchmark training proves effective in enhancing both robustness and generalization.

cs.CL↗

arXiv2025

TeleScope: A Longitudinal Dataset for Investigating Online Discourse and Information Interaction on Telegram

Telegram is a globally popular instant messaging platform known for its strong emphasis on security, privacy, and unique social networking features. It has recently emerged as the host for various cross-domain analysis and research works, such as social media influence, propaganda studies, and extremism. This paper introduces TeleScope, an extensive dataset suite that, to our knowledge, is the largest of its kind. It comprises metadata for about 500K Telegram channels and downloaded message metadata for about 71K public channels, accounting for around 120M crawled messages. We also release channel connections and user interaction data built using Telegram's message-forwarding feature to study multiple use cases, such as information spread and message forwarding patterns. In addition, we provide data enrichments, such as language detection, active message posting periods for each channel, and Telegram entities extracted from messages, that enable online discourse analysis beyond what is possible with the original data alone. The dataset is designed for diverse applications, independent of specific research objectives, and sufficiently versatile to facilitate the replication of social media studies comparable to those conducted on platforms like X (formerly Twitter)

cs.SI↗

arXiv2025

The CLEF-2025 CheckThat! Lab: Subjectivity, Fact-Checking, Claim Normalization, and Retrieval

The CheckThat! lab aims to advance the development of innovative technologies designed to identify and counteract online disinformation and manipulation efforts across various languages and platforms. The first five editions focused on key tasks in the information verification pipeline, including check-worthiness, evidence retrieval and pairing, and verification. Since the 2023 edition, the lab has expanded its scope to address auxiliary tasks that support research and decision-making in verification. In the 2025 edition, the lab revisits core verification tasks while also considering auxiliary challenges. Task 1 focuses on the identification of subjectivity (a follow-up from CheckThat! 2024), Task 2 addresses claim normalization, Task 3 targets fact-checking numerical claims, and Task 4 explores scientific web discourse processing. These tasks present challenging classification and retrieval problems at both the document and span levels, including multilingual settings.

cs.CL↗

arXiv2025

Hidden Entity Detection from GitHub Leveraging Large Language Models

Named entity recognition is an important task when constructing knowledge bases from unstructured data sources. Whereas entity detection methods mostly rely on extensive training data, Large Language Models (LLMs) have paved the way towards approaches that rely on zero-shot learning (ZSL) or few-shot learning (FSL) by taking advantage of the capabilities LLMs acquired during pretraining. Specifically, in very specialized scenarios where large-scale training data is not available, ZSL / FSL opens new opportunities. This paper follows this recent trend and investigates the potential of leveraging Large Language Models (LLMs) in such scenarios to automatically detect datasets and software within textual content from GitHub repositories. While existing methods focused solely on named entities, this study aims to broaden the scope by incorporating resources such as repositories and online hubs where entities are also represented by URLs. The study explores different FSL prompt learning approaches to enhance the LLMs' ability to identify dataset and software mentions within repository texts. Through analyses of LLM effectiveness and learning strategies, this paper offers insights into the potential of advanced language models for automated entity detection.

cs.CL↗

arXiv2024

Public Discourse about COVID-19 Vaccinations: A Computational Analysis of the Relationship between Public Concerns and Policies

Societies worldwide have witnessed growing rifts separating advocates and opponents of vaccinations and other COVID-19 countermeasures. With the rollout of vaccination campaigns, German-speaking regions exhibited much lower vaccination uptake than other European regions. While Austria, Germany, and Switzerland (the DACH region) caught up over time, it remains unclear which factors contributed to these changes. Scrutinizing public discourses can help shed light on the intricacies of vaccine hesitancy and inform policy-makers tasked with making far-reaching decisions: policies need to effectively curb the spread of the virus while respecting fundamental civic liberties and minimizing undesired consequences. This study draws on Twitter data to analyze the topics prevalent in the public discourse. It further maps the topics to different phases of the pandemic and policy changes to identify potential drivers of change in public attention. We use a hybrid pipeline to detect and analyze vaccination-related tweets using topic modeling, sentiment analysis, and a minimum of social scientific domain knowledge to analyze the discourse about vaccinations in the light of the COVID-19 pandemic in the DACH region. We show that skepticism regarding the severity of the COVID-19 virus and towards efficacy and safety of vaccines were among the prevalent topics in the discourse on Twitter but that the most attention was given to debating the theme of freedom and civic liberties. Especially during later phases of the pandemic, when implemented policies restricted the freedom of unvaccinated citizens, increased vaccination uptake could be observed. At the same time, increasingly negative and polarized sentiments emerge in the discourse. This suggests that these policies might have effectively attenuated vaccination hesitancy but were not successfully dispersing citizens' doubts and concerns.

cs.CY↗

arXiv2024

Enhancing Software-Related Information Extraction via Single-Choice Question Answering with Large Language Models

This paper describes our participation in the Shared Task on Software Mentions Disambiguation (SOMD), with a focus on improving relation extraction in scholarly texts through generative Large Language Models (LLMs) using single-choice question-answering. The methodology prioritises the use of in-context learning capabilities of GLMs to extract software-related entities and their descriptive attributes, such as distributive information. Our approach uses Retrieval-Augmented Generation (RAG) techniques and GLMs for Named Entity Recognition (NER) and Attributive NER to identify relationships between extracted software entities, providing a structured solution for analysing software citations in academic literature. The paper provides a detailed description of our approach, demonstrating how using GLMs in a single-choice QA paradigm can greatly enhance IE methodologies. Our participation in the SOMD shared task highlights the importance of precise software citation practices and showcases our system's ability to overcome the challenges of disambiguating and extracting relationships between software mentions. This sets the groundwork for future research and development in this field.

cs.CL↗

arXiv2024

Toward FAIR Semantic Publishing of Research Dataset Metadata in the Open Research Knowledge Graph

Search engines these days can serve datasets as search results. Datasets get picked up by search technologies based on structured descriptions on their official web pages, informed by metadata ontologies such as the Dataset content type of schema.org. Despite this promotion of the content type dataset as a first-class citizen of search results, a vast proportion of datasets, particularly research datasets, still need to be made discoverable and, therefore, largely remain unused. This is due to the sheer volume of datasets released every day and the inability of metadata to reflect a dataset's content and context accurately. This work seeks to improve this situation for a specific class of datasets, namely research datasets, which are the result of research endeavors and are accompanied by a scholarly publication. We propose the ORKG-Dataset content type, a specialized branch of the Open Research Knowledge Graoh (ORKG) platform, which provides descriptive information and a semantic model for research datasets, integrating them with their accompanying scholarly publications. This work aims to establish a standardized framework for recording and reporting research datasets within the ORKG-Dataset content type. This, in turn, increases research dataset transparency on the web for their improved discoverability and applied use. In this paper, we present a proposal -- the minimum FAIR, comparable, semantic description of research datasets in terms of salient properties of their supporting publication. We design a specific application of the ORKG-Dataset semantic model based on 40 diverse research datasets on scientific information extraction.

cs.DL↗

arXiv2024

Dissecting Paraphrases: The Impact of Prompt Syntax and supplementary Information on Knowledge Retrieval from Pretrained Language Models

Pre-trained Language Models (PLMs) are known to contain various kinds of knowledge. One method to infer relational knowledge is through the use of cloze-style prompts, where a model is tasked to predict missing subjects or objects. Typically, designing these prompts is a tedious task because small differences in syntax or semantics can have a substantial impact on knowledge retrieval performance. Simultaneously, evaluating the impact of either prompt syntax or information is challenging due to their interdependence. We designed CONPARE-LAMA - a dedicated probe, consisting of 34 million distinct prompts that facilitate comparison across minimal paraphrases. These paraphrases follow a unified meta-template enabling the controlled variation of syntax and semantics across arbitrary relations. CONPARE-LAMA enables insights into the independent impact of either syntactical form or semantic information of paraphrases on the knowledge retrieval performance of PLMs. Extensive knowledge retrieval experiments using our probe reveal that prompts following clausal syntax have several desirable properties in comparison to appositive syntax: i) they are more useful when querying PLMs with a combination of supplementary information, ii) knowledge is more consistently recalled across different combinations of supplementary information, and iii) they decrease response uncertainty when retrieving known facts. In addition, range information can boost knowledge retrieval performance more than domain information, even though domain information is more reliably helpful across syntactic forms.

cs.CL↗

arXiv2024

TACO -- Twitter Arguments from COnversations

Twitter has emerged as a global hub for engaging in online conversations and as a research corpus for various disciplines that have recognized the significance of its user-generated content. Argument mining is an important analytical task for processing and understanding online discourse. Specifically, it aims to identify the structural elements of arguments, denoted as information and inference. These elements, however, are not static and may require context within the conversation they are in, yet there is a lack of data and annotation frameworks addressing this dynamic aspect on Twitter. We contribute TACO, the first dataset of Twitter Arguments utilizing 1,814 tweets covering 200 entire conversations spanning six heterogeneous topics annotated with an agreement of 0.718 Krippendorff's alpha among six experts. Second, we provide our annotation framework, incorporating definitions from the Cambridge Dictionary, to define and identify argument components on Twitter. Our transformer-based classifier achieves an 85.06\% macro F1 baseline score in detecting arguments. Moreover, our data reveals that Twitter users tend to engage in discussions involving informed inferences and information. TACO serves multiple purposes, such as training tweet classifiers to manage tweets based on inference and information elements, while also providing valuable insights into the conversational reply patterns of tweets.

cs.CL↗

arXiv2023

nuScenes Knowledge Graph -- A comprehensive semantic representation of traffic scenes for trajectory prediction

Trajectory prediction in traffic scenes involves accurately forecasting the behaviour of surrounding vehicles. To achieve this objective it is crucial to consider contextual information, including the driving path of vehicles, road topology, lane dividers, and traffic rules. Although studies demonstrated the potential of leveraging heterogeneous context for improving trajectory prediction, state-of-the-art deep learning approaches still rely on a limited subset of this information. This is mainly due to the limited availability of comprehensive representations. This paper presents an approach that utilizes knowledge graphs to model the diverse entities and their semantic connections within traffic scenes. Further, we present nuScenes Knowledge Graph (nSKG), a knowledge graph for the nuScenes dataset, that models explicitly all scene participants and road elements, as well as their semantic and spatial relationships. To facilitate the usage of the nSKG via graph neural networks for trajectory prediction, we provide the data in a format, ready-to-use by the PyG library. All artefacts can be found here: https://github.com/boschresearch/nuScenes_Knowledge_Graph

cs.CV↗

arXiv2023

GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity Extraction Focused on Machine Learning Models and Datasets

Named Entity Recognition (NER) models play a crucial role in various NLP tasks, including information extraction (IE) and text understanding. In academic writing, references to machine learning models and datasets are fundamental components of various computer science publications and necessitate accurate models for identification. Despite the advancements in NER, existing ground truth datasets do not treat fine-grained types like ML model and model architecture as separate entity types, and consequently, baseline models cannot recognize them as such. In this paper, we release a corpus of 100 manually annotated full-text scientific publications and a first baseline model for 10 entity types centered around ML models and datasets. In order to provide a nuanced understanding of how ML models and datasets are mentioned and utilized, our dataset also contains annotations for informal mentions like "our BERT-based model" or "an image CNN". You can find the ground truth dataset and code to replicate model training at https://data.gesis.org/gsap/gsap-ner.

cs.CL↗

arXiv2023

Large Language Models and Knowledge Graphs: Opportunities and Challenges

Large Language Models (LLMs) have taken Knowledge Representation -- and the world -- by storm. This inflection point marks a shift from explicit knowledge representation to a renewed focus on the hybrid representation of both explicit knowledge and parametric knowledge. In this position paper, we will discuss some of the common debate points within the community on LLMs (parametric knowledge) and Knowledge Graphs (explicit knowledge) and speculate on opportunities and visions that the renewed focus brings, as well as related research topics and challenges.

cs.AI↗