Open AccessCoEPinKB: A Framework to Understand the Connectivity of Entity Pairs in Knowledge Bases
Author(s) -
Javier Guillot Jiménez,
Luiz André P. Paes Leme,
Marco A. Casanova
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/semish.2021.15811
Subject(s) - computer science , ranking (information retrieval) , path (computing) , knowledge base , similarity (geometry) , measure (data warehouse) , data mining , graph , theoretical computer science , information retrieval , similarity measure , base (topology) , rdf , knowledge graph , artificial intelligence , semantic web , mathematics , mathematical analysis , image (mathematics) , programming language
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.