Explaining and suggesting relatedness in knowledge graphs

Knowledge graphs (KGs) are a key ingredient for searching, browsing and knowledge discovery activities. Motivated by the need to harness knowledge available in a variety of KGs, we face the following two problems. First, given a pair of entities defined in some KG, find an explanation of their relatedness. We formalize the notion of relatedness explanation and introduce different criteria to build explanations based on information-theory, diversity and their combinations. Second, given a pair of entities, find other (pairs of) entities sharing a similar relatedness perspective. We describe an implementation of our ideas in a tool, called RECAP, which is based on RDF and SPARQL. We provide an evaluation of RECAP and a comparison with related systems on real-world data.