Relation-oriented faceted search method for knowledge bases

We propose a relation-oriented faceted search method for knowledge bases (KBs) that allows users to explore relations between entities. KBs store a wide range of knowledge about real-world entities in a structured form as (subject, predicate, object). Although it is possible to query entities and relations among entities by specifying appropriate query expressions of SPARQL or keyword queries, the structure and the vocabulary are complicated and it is hard for non-expert users to get the desired information. For this reason, many researchers have proposed faceted search interfaces for KBs. Nevertheless, existing ones are designed for finding entities and are insufficient for finding relations. To this problem, we propose a novel "relation facet" to find relations between entities. To generate it, we apply clustering over predicates based on the Jaccard similarity. We experimentally show the proposed scheme performs better than existing ones in the task of searching relations.