Abstract
Over the recent years, ontologies are widely used in the biomedical domains. However, biomedical ontology heterogeneity problem hamper the cooperation between intelligent applications based on biomedical ontologies. It is crucial to establish correspondences between the heterogeneous biomedical concepts in different ontologies, which is so-called biomedical ontology matching. Approaches based on Multi-Objective Evolutionary Algorithm (MOEA), such as NSGA-II, are emerging as a new methodology to solve the ontology matching problem. In this paper, to further improve the quality of biomedical ontology alignments, a hybrid NSGA-II is proposed, which modifies the knee solutions in the Pareto front by using a local search method. Experiment utilizes two biomedical ontology matching tracks provided by Ontology Alignment Evaluation Initiative (OAEI 2017). The experimental results show that our approach outperforms the participants of OAEI 2017 and NSGA-II based ontology matching technique.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (Nos. 61503082 and 61403121), Natural Science Foundation of Fujian Province (No. 2016J05145), Fundamental Research Funds for the Central Universities (No. 2015B20214), Scientific Research Startup Foundation of Fujian University of Technology (No. GY-Z15007), Scientific Research Development Foundation of Fujian University of Technology (No. GY-Z17162) and Fujian Province Outstanding Young Scientific Researcher Training Project (No. GY-Z160149).
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Xue, X., Chen, J., Chen, J., Chen, D. (2019). A Hybrid NSGA-II for Matching Biomedical Ontology. In: Pan, JS., Ito, A., Tsai, PW., Jain, L. (eds) Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing. IIH-MSP 2018. Smart Innovation, Systems and Technologies, vol 109. Springer, Cham. https://doi.org/10.1007/978-3-030-03745-1_1
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DOI: https://doi.org/10.1007/978-3-030-03745-1_1
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