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Efficient Ontology Meta-Matching Based on Interpolation Model Assisted Evolutionary Algorithm

Author

Listed:
  • Xingsi Xue

    (Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China)

  • Qi Wu

    (College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030000, China)

  • Miao Ye

    (School of Information and Communication, Guilin University of Electronic Technology, Guilin 540014, China)

  • Jianhui Lv

    (Pengcheng Laboratory, Shenzhen 518038, China)

Abstract

Ontology is the kernel technique of the Semantic Web (SW), which models the domain knowledge in a formal and machine-understandable way. To ensure different ontologies’ communications, the cutting-edge technology is to determine the heterogeneous entity mappings through the ontology matching process. During this procedure, it is of utmost importance to integrate different similarity measures to distinguish heterogeneous entity correspondence. The way to find the most appropriate aggregating weights to enhance the ontology alignment’s quality is called ontology meta-matching problem, and recently, Evolutionary Algorithm (EA) has become a great methodology of addressing it. Classic EA-based meta-matching technique evaluates each individual through traversing the reference alignment, which increases the computational complexity and the algorithm’s running time. For overcoming this drawback, an Interpolation Model assisted EA (EA-IM) is proposed, which introduces the IM to predict the fitness value of each newly generated individual. In particular, we first divide the feasible region into several uniform sub-regions using lattice design method, and then precisely evaluate the Interpolating Individuals (INIDs). On this basis, an IM is constructed for each new individual to forecast its fitness value, with the help of its neighborhood. For testing EA-IM’s performance, we use the Ontology Alignment Evaluation Initiative (OAEI) Benchmark in the experiment and the final results show that EA-IM is capable of improving EA’s searching efficiency without sacrificing the solution’s quality, and the alignment’s f-measure values of EA-IM are better than OAEI’s participants.

Suggested Citation

  • Xingsi Xue & Qi Wu & Miao Ye & Jianhui Lv, 2022. "Efficient Ontology Meta-Matching Based on Interpolation Model Assisted Evolutionary Algorithm," Mathematics, MDPI, vol. 10(17), pages 1-20, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3212-:d:907302
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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.
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