The Internet is nowadays a fantastic source of information thanks to the quantity of the information it provides and its dynamicity. However, these features also represent challenges when we want to consider trustworthy information only. On the Internet, the process of verifying information, known as fact-checking, cannot be performed by human experts given the scale of the information that should be manually checked, and the speed to which it changes. In this paper, we propose an approach to evaluate the trustworthiness of online information modeled as RDF Triples. Given a use case, we select a specific ontology (in the following we use movie reviews as a use case) and match its object properties with WordNet. This allows us to understand, for each input triple, which class the subject and the object belong to. We associate SPARQL queries to each class, which are then used by our approach to search for additional evidences in Wikidata. By doing so, our approach generates feature vectors that are used by machine learning classification models to predict the trustworthiness of new input triples. Experiments on real movie data show that our approach provides results that are on par or better than the state of the art in fact checking.