A distributed framework to investigate the entity relatedness problem in large RDF knowledge bases

The entity relatedness problem refers to the question of exploring a knowledge base, represented as an RDF graph, to discover and understand how two entities are connected. This question can be addressed by implementing a path search strategy, which combines an entity similarity measure, with an expansion limit, to reduce the path search space and a path ranking measure to order the relevant paths between a given pair of entities in the RDF graph. This paper first introduces DCoEPinKB, an in-memory distributed framework that addresses the entity relatedness problem. Then, it presents an evaluation of path search strategies using DCoEPinKB over real data collected from DBpedia. The results provide insights about the performance of the path search strategies.

CoEPinKB: A Framework to Understand the Connectivity of Entity Pairs in Knowledge Bases

A knowledge base, expressed using the Resource Description Framework (RDF), can be viewed as a graph whose nodes represent entities and whose edges denote relationships. The entity relatedness problem refers to the problem of discovering and understanding how two entities are related, directly or indirectly, that is, how they are connected by paths in a knowledge base. Strategies designed to solve the entity relatedness problem typically adopt an entity similarity measure to reduce the path search space and a path ranking measure to order and filter the list of paths returned. This paper presents a framework, called CoEPinKB, that supports the empirical evaluation of such strategies. The proposed framework allows combining entity similarity and path ranking measures to generate different path search strategies. The main goals of this paper are to describe the framework and present a performance evaluation of nine different path search strategies.