Data veracity is one of the main issues regarding web data. Facing fake news proliferation and disinformation dangers, Truth Discovery models can be used to assess this veracity by estimating value confidence and source trustworthiness through analysis of claims on the same real-world entities provided by different sources. This treatment is crucial within an automated knowledge extraction process, in particular if resulting knowledge bases (KB) are devoted to be used in decision processes. 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 ontologies). This article proposes two ways to reinforce some value confidences and thus source trustworthiness calculus 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.
truth discovery, ontologies, semantic web, value confidence, source trustworthiness, association rule learning, reasoning
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