A Comparison of Multi-Objective Evolutionary Algorithms for the Ontology Meta-Matching Problem

Abstract

In recent years, several ontology-based systems have been developed for data 
integration purposes. The principal task of these systems is to accomplish an 
ontology alignment process capable of matching two ontologies used for modeling 
heterogeneous data sources. Unfortunately, in order to perform an efficient ontology 
alignment, it is necessary to address a nested issue known as ontology meta-matching 
problem consisting in appropriately setting some regulating parameters. Over years, 
evolutionary algorithms are appeared to be the most suitable methodology to address 
this problem. However, almost all of existing approaches work with a single function to 
be optimized even though a possible solution for the ontology meta-matching problem 
can be viewed as a compromise among different objectives. Therefore, approaches based 
on multi-objective optimization are emerging as techniques more efficient than conventional 
evolutionary algorithms in solving the meta-matching problem. The aim of this paper is to 
perform a systematic comparison among well-known multi-objective Evolutionary Algorithms 
(EAs) in order to study their effects in solving the meta-matching problem. As shown through 
computational experiments, among the compared multi-objective EAs, OMOPSO statistically 
provides the best performance in terms of the well-known measures such as hypervolume, 
Delta index and coverage of two sets.