The Web of Data has been introduced as a novel scheme for imposing structured data on the Web. This renders data easily understandable by human beings and seamlessly processable by machines at the same time. The recent boom in Linked Data facilitates a new stream of data-intensive applications that leverage the knowledge available in semantic datasets such as DBpedia and Freebase. These latter are well known encyclopedic collections of data that can be used to feed a content-based recommender system. In this paper we investigate how the choice of one of the two datasets may influence the performance of a recommendation engine not only in terms of precision of the results but also in terms of their diversity and novelty. We tested four different recommendation approaches exploiting both DBpedia and Freebase in the music domain.