Data veracity is one of the main issues regarding Web data. Automatically identifying reliable information is a critical task within a knowledge extraction process, in particular if resulting knowledge bases (KB) are devoted to be used in decision processes. Truth discovery models address it comparing information provided by multiple sources. They assume that true information is provided by reliable sources and, vice versa, reliable sources provide true information. In this way, they are able to identify which are the true claims among a set of conflicting ones. To the best of our knowledge, existing methods do not consider prior knowledge that can be extracted from an ontology. Based on the type of information that is considered, important suggestions that can facilitate the identification of true claims can be recognized. In our study, we analyze the impact of incorporating into truth discovery model the information associated with concepts' hierarchy and frequent patterns that are extracted from KB using association rule learning techniques. Empirical experiments on synthetic and real world datasets show advantages and disadvantages of proposed models.