Abstract
This study exploits knowledge expressed in RDF Knowledge Bases (KBs) to enhance Truth Discovery (TD) performances. TD aims to identify facts (true claims) when conflicting claims are made by several sources. Based on the assumption that true claims are provided by reliable sources and reliable sources provide true claims, TD models iteratively compute value confidence and source trustworthiness in order to determine which claims are true. We propose a model that exploits the knowledge extracted from an existing RDF KB in the form of rules. These rules are used to quantify the evidence given by the RDF KB to support a claim. This evidence is then integrated into the computation of the confidence value to improve its estimation. Enhancing TD models efficiently obtains a larger set of reliable facts that vice versa can populate RDF KBs. Empirical experiments on real-world datasets showed the potential of the proposed approach, which led to an improvement of up to 18% compared to the model we modified.
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