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Improving Ontology Alignment through Genetic Algorithms
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Author(s): José Manuel Vázquez Naya (University of A Coruña, Spain), Marcos Martínez Romero (University of A Coruña, Spain), Javier Pereira Loureiro (University of A Coruña, Spain), Cristian R. Munteanu (University of A Coruña, Spain)and Alejandro Pazos Sierra (University of A Coruña, Spain)
Copyright: 2010
Pages: 20
Source title:
Soft Computing Methods for Practical Environment Solutions: Techniques and Studies
Source Author(s)/Editor(s): Marcos Gestal Pose (University of A Coruna, Spain)and Daniel Rivero Cebrián (University of A Coruna, Spain)
DOI: 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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