Matching Ontologies through Multi-Objective Evolutionary Algorithm with Relevance Matrix

Hai Zhu, Xingsi Xue and Hongfeng Wang
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Hai Zhu: School of Network Engineering, Zhoukou Normal University, Zhoukou 466001, China
Xingsi Xue: Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China
Hongfeng Wang: School of Network Engineering, Zhoukou Normal University, Zhoukou 466001, China

Mathematics, 2022, vol. 10, issue 12, 1-12

Abstract: The ultimate goal of semantic web (SW) is to implement mutual collaborations among ontology-based intelligent systems. To this end, it is necessary to integrate those domain-independent and cross-domain ontologies by finding the correspondences between their entities, which is the so-called ontology matching. To improve the quality of ontology alignment, in this work, the ontology matching problem is first defined as a sparse multi-objective optimization problem (SMOOP), and then, a multi-objective evolutionary algorithm with a relevance matrix (MOEA-RM) is proposed to address it. In particular, a relevance matrix (RM) is presented to adaptively measure the relevance of each individual’s genes to the objectives, which is applied in MOEA’s initialization, crossover and mutation to ensure the population’s sparsity and to speed up the the algorithm’s convergence. The experiment verifies the performance of MOEA-RM by comparing it with the state-of-the-art ontology matching techniques, and the experimental results show that MOEA-RM is able to effectively address the ontology matching problem with different heterogeneity characteristics.

Keywords: ontology matching; sparse multi-objective optimization problem; multi-objective evolutionary algorithm; relevance matrix (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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