Optimizing ontology alignments by using genetic algorithms

Jorge Martinez-Gil, E Alba, J F Aldana-Montes

Full text: http://137.226.34.227/Publications/CEUR-WS/Vol-419/paper2.pdf

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Abstract

In this work we present GOAL (Genetics for Ontology Alignments) a new approach to compute the optimal ontology alignment function for a given ontology input set. Although this problem could be solved by an exhaustive search when the number of similarity measures is low, our method is expected to scale better for a high number of measures. Our approach is a genetic algorithm which is able to work with several goals: maximizing the alignment precision, maximizing the alignment recall, maximizing the f-measure or reducing the number of false positives. Moreover, we test it here by combining some cutting-edge similarity measures over a standard benchmark, and the results obtained show several advantages in relation to other techniques.

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