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Improving Ontology Alignment through Genetic Algorithms

Improving Ontology Alignment through Genetic Algorithms

José Manuel Vázquez Naya, Marcos Martínez Romero, Javier Pereira Loureiro, Cristian R. Munteanu, Alejandro Pazos Sierra
Copyright: © 2010 |Pages: 20
ISBN13: 9781615208937|ISBN10: 1615208933|ISBN13 Softcover: 9781616923310|EISBN13: 9781615208944
DOI: 10.4018/978-1-61520-893-7.ch015
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MLA

Vázquez Naya, José Manuel, et al. "Improving Ontology Alignment through Genetic Algorithms." Soft Computing Methods for Practical Environment Solutions: Techniques and Studies, edited by Marcos Gestal Pose and Daniel Rivero Cebrián, IGI Global, 2010, pp. 240-259. https://doi.org/10.4018/978-1-61520-893-7.ch015

APA

Vázquez Naya, J. M., Martínez Romero, M., Loureiro, J. P., Munteanu, C. R., & Pazos Sierra, A. (2010). Improving Ontology Alignment through Genetic Algorithms. In M. Gestal Pose & D. Rivero Cebrián (Eds.), Soft Computing Methods for Practical Environment Solutions: Techniques and Studies (pp. 240-259). IGI Global. https://doi.org/10.4018/978-1-61520-893-7.ch015

Chicago

Vázquez Naya, José Manuel, et al. "Improving Ontology Alignment through Genetic Algorithms." In Soft Computing Methods for Practical Environment Solutions: Techniques and Studies, edited by Marcos Gestal Pose and Daniel Rivero Cebrián, 240-259. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-61520-893-7.ch015

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Abstract

Ontology alignment is recognized as a fundamental process to achieve an adequate interoperability between people or systems that use different, overlapping ontologies to represent common knowledge. This process consists of finding the semantic relations between different ontologies. There are different techniques conceived to measure the semantic similarity of elements from separate ontologies, which must be adequately combined in order to obtain precise and complete results. Nevertheless, combining multiple measures into a single similarity metric is a complex problem, which has been traditionally solved using weights determined manually by an expert, or calculated through general methods that does not provide optimal results. In this chapter, a genetic algorithm based approach to find out how to aggregate different similarity metrics into a single measure is presented. Starting from an initial population of individuals, each one representing a specific combination of measures, the algorithm finds the combination that provides the best alignment quality.

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