Premium
A hybrid evolutionary approach for solving the ontology alignment problem
Author(s) -
Acampora Giovanni,
Loia Vincenzo,
Salerno Saverio,
Vitiello Autilia
Publication year - 2012
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20517
Subject(s) - computer science , ontology , semantic heterogeneity , ontology alignment , domain (mathematical analysis) , interoperability , process (computing) , semantic interoperability , component (thermodynamics) , artificial intelligence , process ontology , semantic web , theoretical computer science , ontology based data integration , world wide web , mathematics , mathematical analysis , operating system , physics , thermodynamics , philosophy , epistemology
Ontologies are recognized as a fundamental component for enabling interoperability across heterogeneous systems and applications. Indeed, they try to fit a common understanding of concepts in a particular domain of interest to support the exchange of information among people, artificial agents, and distributed applications. Unfortunately, because of human subjectivity, various ontologies related to the same application domain may use different terms for the same meaning or may use the same term to mean different things, raising the so‐called heterogeneity problem. The ontology alignment process tries to solve this semantic gap by individuating a collection of similar entities belonging to different ontologies and enabling a full comprehension among different actors involved in a given knowledge exchanging. However, the complexity of the alignment task, especially for large ontologies, requires an automated and effective support for computing high‐quality alignments. The aim of this paper is to propose a memetic algorithm to perform an efficient matching process capable of computing a suboptimal alignment between two ontologies. As shown by experiments, the memetic approach is more suitable for ontology alignment problem than a classical evolutionary technique such as genetic algorithms. © 2012 Wiley Periodicals, Inc.