Knowledge graphs (KGs) are a key ingredient fo rsearching, 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.
Explaining and Suggesting Relatedness in Knowledge Graphs
G Pirro'
2015
Abstract
Knowledge graphs (KGs) are a key ingredient fo rsearching, 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
