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Ontology, as a structured conceptual model of knowledge representation and storage, has widely been used in biomedical and pharmaceutical research. The nature of the ontology application is to get the similarity between ontology vertices, and thus reveal the similarity of their corresponding concepts and intrinsic relationships. The similarity for all pairs of vertices forms a similarity matrix of ontology, and the aim of ontology algorithm is to obtain the optimal similarity matrix. In this paper, we propose a new algorithm to get a similarity matrix in terms of learning the optimal similarity kernel function for ontology. The simulation experiment is designed for biology â€œGoâ€ ontology application, and the result data reveal that new algorithm is efficient for such task.

Algorithm of Ontology Similarity Measure Based on Similarity Kernel Learning