SELEcTor: Discovering Similar Entities on LinkEd DaTa by Ranking Their Features

Several approaches have been used in the last years to compute similarity between entities. In this paper, we present a novel approach to compute similarity between entities using their features available as Linked Data. The key idea of the proposed framework, called SELEcTor, is to exploit ranked lists of features extracted from Linked Data sources as a representation of the entities we want to compare. The similarity between two entities is thus mapped to the problem of comparing two ranked lists. Our experiments, conducted with museum data from DBpedia, demonstrate that SELEcTor achieves better accuracy than stateof-the-art methods