A system for relation-oriented faceted search over knowledge bases

Abstract

Purpose: The purpose of this paper is to propose a scheme that allows users to interactively explore relations between entities in knowledge bases (KBs). 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. Design/methodology/approach: To this problem, the authors propose a novel “relation facet” to find relations between entities. To generate it, they applied clustering on predicates for grouping those predicates that are connected to common objects. Having generated clusters of predicates, the authors generated a facet according to the result. Specifically, they proposed to use a couple of clustering algorithms, namely, agglomerative hierarchical clustering (AHC) and CANDECOMP/PARAFAC (CP) tensor decomposition which is one of the tensor decomposition methods. Findings: The authors experimentally show test the performance of clustering methods and found that AHC performs better than tensor decomposition. Besides, the authors conducted a user study and show that their proposed scheme performs better than existing ones in the task of searching relations. Originality/value: The authors propose a relation-oriented faceted search method for KBs that allows users to explore relations between entities. As far as the authors know, this is the first method to focus on the exploration of relations between entities.