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Matching Ontologies through Multi-Objective Evolutionary Algorithm with Relevance Matrix

Author

Listed:
  • 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)

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.

Suggested Citation

  • Hai Zhu & Xingsi Xue & Hongfeng Wang, 2022. "Matching Ontologies through Multi-Objective Evolutionary Algorithm with Relevance Matrix," Mathematics, MDPI, vol. 10(12), pages 1-12, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:12:p:2077-:d:839560
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    References listed on IDEAS

    as
    1. Nicola Guarino & Daniel Oberle & Steffen Staab, 2009. "What Is an Ontology?," International Handbooks on Information Systems, in: Steffen Staab & Rudi Studer (ed.), Handbook on Ontologies, pages 1-17, Springer.
    2. Xingsi Xue & Junfeng Chen, 2018. "A Preference-Based Multi-Objective Evolutionary Algorithm for Semiautomatic Sensor Ontology Matching," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 9(2), pages 1-14, April.
    3. Jérôme David, 2007. "Association Rule Ontology Matching Approach," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 3(2), pages 27-49, April.
    Full references (including those not matched with items on IDEAS)

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