Un cadre paraconsistant pour l'{é}valuation de similarit{é} dans les bases de connaissances
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915488450215936 |
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| author | Medina, José-Luis Vilchis |
| author_facet | Medina, José-Luis Vilchis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08433 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Un cadre paraconsistant pour l'{é}valuation de similarit{é} dans les bases de connaissances Medina, José-Luis Vilchis Databases Information Theory Logic in Computer Science Symbolic Computation Category Theory 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. |
| title | Un cadre paraconsistant pour l'{é}valuation de similarit{é} dans les bases de connaissances |
| topic | Databases Information Theory Logic in Computer Science Symbolic Computation Category Theory |
| url | https://arxiv.org/abs/2509.08433 |