2025/09/10 by José-Luis Vilchis Medina, Medina, José-Luis Vilchis
Computer Science · #Logic, Reasoning, and Knowledge #Computability, Logic, AI Algorithms #Rough Sets and Fuzzy Logic
paper · pdf · doi:10.48550/arxiv.2509.08433
This article proposes a paraconsistent framework for evaluating similarity in knowledge bases. Unlike classical approaches, this framework explicitly integrates contradictions, enabling a more robust and interpretable similarity measure. A new measure S^* is introduced, which penalizes inconsistencies while rewarding shared properties. Paraconsistent super-categories ΞK^* are defined to hierarchically organize knowledge entities. The model also includes a contradiction extractor E and a repair mechanism, ensuring consistency in the evaluations. Theoretical results guarantee reflexivity, symmetry, and boundedness of S^* . This approach offers a promising solution for managing conflicting knowledge, with perspectives in multi-agent systems.