Améliorer la recherche de vérité en exploitant la connaissance de domaines via les ontologies et les règles d'association

Data veracity is one of the main issues regarding web data . F cing fake news proliferation and disinformation dangers, Truth Discovery mode ls can be used to assess this veracity by estimating value confidence and source trustwor thiness through analysis of claims on the same real-world entities provided by different sources. Thi treatment is crucial within an automated knowledge extraction process, in particular if resu lting knowledge bases (KB) are devoted to be used in decision process es. Many studies have been conducted in Truth Discovery domain; however none of them, to our knowledge, take into account the a priori knowledge that may exist regarding a domain (e.g., domain on tologies). This article proposes two ways to reinforce some value confidences and thus source trustworthiness calculus 374 RIA. Volume 32 – n° 3/2018 during this process: the first one considers the concepts ’ hierarchy and the second one exploits patterns that are extracted from KB using association rule learning techniques. Both approaches are validated and tested using benchmarks, that are freely available as well as the source code. MOTS-CLÉS : détection de vérité, ontologies, web sémantique, confiance, f iabilité des sources, détection de règles, raisonnement.

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