This study exploits knowledge expressed by RDF Knowledge Bases (KBs) to enhance Truth Discovery performance. Truth Discovery aims to identify facts (true claims) when conflicting claims are provided by several sources. Based on the assumption that true claims are provided by reliable sources and reliable sources provide true claims, Truth Discovery models iteratively compute value confidence and source trustworthiness in order to determine which claims are true. We propose a model that takes advantage of the knowledge extracted from an existing RDF KB in form of rules. These rules are used to quantify the evidence given by the RDF KB to support a claim. Then, this evidence is integrated in the computation of value confidence to improve its estimation. Enhancing truth discovery models allows to efficiently obtain a larger set of reliable facts that vice versa can be used to populate RDF KBs. Empirical experiments on real-world datasets show the potential of the proposed approach which lead to an improvement up to 18% w.r.t. the model we modified.