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Matching heterogeneous ontologies based on multi-strategy adaptive co-firefly algorithm

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Abstract

As an effective method of addressing ontology heterogeneity problem, ontology matching becomes increasingly important for knowledge sharing and inter-system communication. Ontology meta-matching, which aims at finding optimal ways of integrating different similarity measures, is an effective method of determining high-quality ontology alignment. However, the existing ontology meta-matching techniques suffer from the following defects: first, most of them are depending on the reference alignment that ought to be given by experts in advance, which is not available in the practical scenarios; second, they tend to get stuck in the local optima, which makes the alignment unsatisfactory. In order to solve the above problems, in this work, an optimization model for ontology meta-matching problem is constructed on the basis of a new proposed evaluation metric on the alignment’s quality. After that, a multi-strategy adaptive co-firefly algorithm (MACFA), which is able to trade off the algorithm’s exploitation and exploration, is proposed to overcome the premature convergence. The testing cases in Ontology Alignment Evaluation Initiative (OAEI) is utilized to verify the effectiveness of our approach. Experimental results show that the optimization model as well as MACFA improves the ontology alignment’s quality, and compared with OAEI’s participants, the proposed matching system achieves competitive results.

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Notes

  1. http://oaei.ontologymatching.org/

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  1. College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030024, Shanxi, China

    Xin Zhou, Qing Lv & Aifeng Geng

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  1. Xin Zhou
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  2. Qing Lv
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  3. Aifeng Geng
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Correspondence to Qing Lv.

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Zhou, X., Lv, Q. & Geng, A. Matching heterogeneous ontologies based on multi-strategy adaptive co-firefly algorithm. Knowl Inf Syst 65, 2619–2644 (2023). https://doi.org/10.1007/s10115-023-01845-2

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