[2603.02858] LLM-based Argument Mining meets Argumentation and Description Logics: a Unified Framework for Reasoning about Debates

Computer Science > Artificial Intelligence arXiv:2603.02858 (cs) [Submitted on 3 Mar 2026] Title:LLM-based Argument Mining meets Argumentation and Description Logics: a Unified Framework for Reasoning about Debates Authors:Gianvincenzo Alfano, Sergio Greco, Lucio La Cava, Stefano Francesco Monea, Irina Trubitsyna View a PDF of the paper titled LLM-based Argument Mining meets Argumentation and Description Logics: a Unified Framework for Reasoning about Debates, by Gianvincenzo Alfano and 4 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) achieve strong performance in analyzing and generating text, yet they struggle with explicit, transparent, and verifiable reasoning over complex texts such as those containing debates. In particular, they lack structured representations that capture how arguments support or attack each other and how their relative strengths determine overall acceptability. We encompass these limitations by proposing a framework that integrates learning-based argument mining with quantitative reasoning and ontology-based querying. Starting from a raw debate text, the framework extracts a fuzzy argumentative knowledge base, where arguments are explicitly represented as entities, linked by attack and support relations, and annotated with initial fuzzy strengths reflecting plausibility w.r.t. the debate's context. Quantitative argumentation semantics are then applied to compute final argument strengths by propagating the effects ...