A hybrid evolutionary approach for solving the ontology alignment problem

Abstract

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

A comparison of multi-objective evolutionary algorithms for the ontology meta-matching problem

“…); a linguistic one determines a similarity value between two entities by taking into account semantic relations such as synonymy and hypernymy; structural ones compute a similarity value between two entities by considering their kinship (parents and children). In this work, we consider the following similarity measures extracted by [16]: Entity Name Distance Measure and Comment Distance Measure among lexical similarity measures, Hierarchy Distance Measure and Domain and Range Restrictions Distance Measure belonging to structural ones and the linguistic Word Net Synonymy Name Distance Measure. Since the application of a single measure is often not enough to produce an acceptable output alignment, the common technique is to combine different matchers to compute a single aggregated similarity value.…”

Improving agent interoperability through a memetic ontology alignment: A comparative study

“…Therefore, hereafter, we describe the chromosome structure representing an alignment, the fitness function which allows the evaluation of each alignment, and the integrated local search process so as defined in [5].…”

“…In particular, the investigated different memetic approaches are based on the memetic ontology alignment system presented in [5]. This system aligns two ontologies 1 and 2 and produces in output an alignment as follows.…”

“…Therefore, in the last years, several approaches aimed to efficiently solve the semantic heterogeneity problem have been proposed [4]. Among them, an innovate approach [5] based on memetic algorithms has been proved to be particularly efficient and more performing than a genetic counterpart. Memetic algorithms (MA) [6] are population-based search methods which combine evolutionary algorithms with local search techniques for further refining population individuals and, consequently, increasing the convergence speed of the evolutionary process.…”

Naturally selecting solutions

“…The remaining offspring are copies of solutions from the previous generation. 48 With the exception of offspring produced through elitism, all offspring produced through crossover or otherwise are subjected to mutation. Typically a value in the range 60-90% is used for the crossover rate.…”