Un cadre paraconsistant pour l'{é}valuation de similarit{é} dans les bases de connaissances

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 $S^{\ast }$ is introduced, which penalizes inconsistencies while rewarding shared properties. Paraconsistent super-categories ${\mathrm{\Xi }}_K^{\ast }$ 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 $S^{\ast }$. This approach offers a promising solution for managing conflicting knowledge, with perspectives in multi-agent systems.