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 of supports and attacks. These results are then embedded into a fuzzy description logic setting, enabling expressive query answering through efficient rewriting techniques. The proposed approach provides a transparent, explainable, and formally grounded method for analyzing debates, overcoming purely statistical LLM-based analyses.
Figures & tables
Figure 1: FAKB workflow: from raw unstructured texts to FAKB generation and querying.
Figure 2: An example of updated FAbox, visualized in the form of a knowledge graph, and extracted from an extended version of debate D of Example 1 . Nodes are color-coded by type: blue for entities, red for argument identifiers, and yellow for concepts. Similarly, edges represent specific relations: green/red denotes support/attacks, while dark blue, light blue, and yellow represent authorship, role instances, and concept instances, respectively. Upper/lower numbers within each argument denote its initial/updated strength. For optimal visualization, the arguments’ text has been omitted, yet they can be found in Section 5 . A full-screen visualization also including additional entities and full texts is provided in the Appendix.
Table 1: Comparison between our approach (left) vs Prompt-based (right) for relation strength estimation on argument pairs (ai,aj) in D1 , with i>j . Red-/Green-colored cells refer to attacks/supports, while grey-shaded cells marked with ‘N’ indicate the absence of a relation. Numeric values denote the relation strength. Solid (resp., dashed) borders refer to pairs of arguments stated by the same author (resp., different authors).
Appendix figures & tables1 asset
Supplementary material from the paper’s appendix.
Appendix
Figure 3: An example of updated FAbox, visualized in the form of a knowledge graph, and extracted from an extended version of debate D of Example 1 . Nodes are color-coded by type: violet for text of arguments, blue for entities, red for argument identifiers, and yellow for concepts. Similarly, edges represent specific relations: green/red denotes support/attacks, while dark blue, light blue, and yellow represent authorship, role instances, and concept instances, respectively. Upper/lower numbers within each argument denote its initial/updated strength.