Author(s): Valentina Beretta, Sébastien Harispe, Sylvie Ranwez, Isabelle Mougenot
Abstract: This study leverages information richness of RDF Knowledge Bases (KBs) to improve Truth Discovery models. These models address the problem of identifying facts when conflicting claims are provided by several sources. Assuming that true claims are provided by reliable sources and reliable sources provide true claims, they iteratively compute value confidence and source trustworthiness in order to establish which claims are true. We propose a model that benefits from the knowledge expressed by an existing RDF KB in the form of rules quantifying the evidence that supports a claim. Then, this quantity is used to improve the value confidence estimation. Enhancing truth discovery performance allows to efficiently obtain a larger set of reliable facts that reciprocally can be used to populate RDF KBs. Empirical experiments on synthetic datasets show the potential of our approach.
Keywords: Truth Discovery; RDF KBs; Rule Mining; Source Trustworthiness; Value Confidence