Abstract
Because of the subjectivity of different ontology engineers, one concept can be expressed in different ways, which leads to the so-called ontology heterogeneity problem. Ontology matching uses the similarity measures to distinguish the correspondence between entities in two ontologies, which is regarded as a feasible method to solve this problem. In order to get more accurate and complete alignment, different kinds of similarity measures need to be adequately combined, and how to determine the optimal aggregating weights is the so-called ontology meta-matching problem. Evolutionary algorithm (EA) can represent an effective methodology for addressing this problem, but due to the decline in population diversity during the evolving process, the conventional EA suffers from the premature convergence. To overcome this drawback, we introduce an adaptive selection strategy. By monitoring the variation of individual fitness in the population, the selection pressure can be dynamically adjusted to maintain the diversity of population thus to avoid the premature convergence of the population. In the experiment, we compare our approach with original EA-based ontology matching technique and the experimental results show the effectiveness of our proposal.
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Lv, Q., Zhou, X., Li, H. (2021). Optimizing Ontology Alignments Through Evolutionary Algorithm with Adaptive Selection Strategy. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_88
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DOI: https://doi.org/10.1007/978-3-030-69717-4_88
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