{"ID":2872525,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.08433","arxiv_id":"2509.08433","title":"Un cadre paraconsistant pour l'{é}valuation de similarit{é} dans les bases de connaissances","abstract":"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 $ Ξ_K^* $ 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.","short_abstract":"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 rewardi...","url_abs":"https://arxiv.org/abs/2509.08433","url_pdf":"https://arxiv.org/pdf/2509.08433v1","authors":"[\"José-Luis Vilchis Medina\"]","published":"2025-09-10T09:26:50Z","proceeding":"cs.DB","tasks":"[\"cs.DB\",\"cs.IT\",\"cs.LO\",\"cs.SC\",\"math.CT\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}