Building Relatedness Explanations from Knowledge Graphs

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Giuseppe Pirrò

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Guest Editors Knowledge Graphs 2018

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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 problem of building relatedness explanations. Relatedness explanations are graphs that can explain how a pair of entities is related in a KG and can be used in a variety of tasks; from exploratory search to query answering. We formalize the notion of relatedness explanation and present two algorithms. The first, E4D (Explanations from Data) assembles explanations starting from paths in the data. The second algorithm E4S (Explanations from Schema), starting from the KG schema, allows to find paths driven by a specific relatedness perspective. We describe different criteria to build explanations based on information-theory, diversity and their combination. As a concrete application, we introduce relatedness-based KG querying, which revisits the query-by-example paradigm from the perspective of relatedness explanations. We implemented all machineries in the RECAP tool, which is based on RDF and SPARQL. We discuss an evaluation of our explanation building algorithms and a comparison of RECAP with related systems on real-world data.
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