Evolutionary algorithm based biomedical ontology matching technique
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Abstract:
Since biomedical ontologies own large-scale concepts and complex relationships among them, the existing ontology matching techniques are not able to determine the biomedical alignment efficiently. To tackle this challenge, a mathematical optimal model for biomedical ontology matching problem is first constructed, and then an evolutionary algorithm (EA) based biomedical ontology matching technique is proposed to determine the optimal alignment. In particular, when solving the biomedical ontology matching problem, a novel biomedical concept similarity measure is utilized to ensure the quality of the alignment, and a reasoning-based concept pruning approach is used to reduce the algorithm's search space and improve its efficiency. The experimental results show that EA-based biomedical ontology matching technique is able to match the biomedical ontologies effectively and efficiently.
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Supported by National Natural Science Foundation of China (62172095), National Natural Scientific Foundation of Fujian Provnce （2020J01875） and Fujian Province Undergraduate Universities Teaching Reform Research Project (FBJG20190156).
