2026-07-23T21:40:31Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:25215 2026-01-21T10:23:01Z ddc:004 open_access

Mining Inter-Document Argument Structures in Scientific Papers for an Argument Web Ruosch, Florian Sarasua, Cristina Bernstein, Abraham Argument Mining Large Language Models Knowledge Graphs Link Prediction In Argument Mining, predicting argumentative relations between texts (or spans) remains one of the most challenging aspects, even more so in the cross-document setting. This paper makes three key contributions to advance research in this domain. We first extend an existing dataset, the Sci-Arg corpus, by annotating it with explicit inter-document argumentative relations, thereby allowing arguments to be distributed over several documents forming an Argument Web; these new annotations are published using Semantic Web technologies (RDF, OWL). Second, we explore and evaluate three automated approaches for predicting these inter-document argumentative relations, establishing critical baselines on the new dataset. We find that a simple classifier based on discourse indicators with access to context outperforms neural methods. Third, we conduct a comparative analysis of these approaches for both intra- and inter-document settings, identifying statistically significant differences in results that indicate the necessity of distinguishing between these two scenarios. Our findings highlight significant challenges in this complex domain and open crucial avenues for future research on the Argument Web of Science, particularly for those interested in leveraging Semantic Web technologies and knowledge graphs to understand scholarly discourse. With this, we provide the first stepping stones in the form of a benchmark dataset, three baseline methods, and an initial analysis for a systematic exploration of this field relevant to the Web of Data and Science. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Florian Ruosch and Cristina Sarasua and Abraham Bernstein 2025 Is Part Of TGDK, Volume 3, Issue 3 (2025). Transactions on Graph Data and Knowledge, Volume 3, Issue 3 Article Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/TGDK.3.3.4 urn:nbn:de:0030-drops-252159 https://drops.dagstuhl.de/entities/document/10.4230/TGDK.3.3.4 eng https://creativecommons.org/licenses/by/4.0/legalcode