Optimizing ontology alignment through hybrid population-based incremental learning algorithm

Abstract

Ontology matching is an effective technique to solve the ontology heterogeneous problem in Semantic Web. Since different ontology matchers do not necessarily find the same correct correspondences, usually several competing matchers are applied to the same pair of entities in order to increase evidence towards a potential match or mismatch. How to select, combine and tune various ontology matchers to obtain the high quality ontology alignment is one of the main challenges in ontology matching domain. Recently, Evolutionary Algorithms (EA) has become the most suitable methodology to face this challenge, however, the huge memory consumption, slow convergence and premature convergence limit its application and reduce the solution’s quality. To this end, in this paper, we propose a Hybrid Population-based Incremental Learning algorithm (HPBIL) to automatically select, combine and tune different ontology matchers, which can overcome three drawbacks of EA based ontology matching techniques and improve the ontology alignment’s quality. In one hand, HPBIL makes use of a probabilistic representation of the population to perform the optimization process, which can significantly reduce EA’s the memory consumption and the possibility of the premature convergence. In the other, we introduce the local search strategy into PBIL’s evolving process to trade off its exploration and exploitation, and this marriage between global search and local search is helpful to reduce the runtime. In the experiment, we utilize different scale testing cases provided by the Ontology Alignment Evaluation Initiative (OAEI 2016) to test HPBIL’s performance, and the experimental results show that HPBIL’s results significantly outperform other EA based ontology matching techniques and top-performers of the OAEI competitions.