Abstract: In view of the problems of low efficiency and large user recommendation error in the process of semantic search, we proposed a semantic search algorithm based on extraction rules and ontology mapping. Firstly, according to user’s requirements in semantic search, the semantic elements and attributes were extracted to solve the defect of low utilization rate of data. Secondly, we established a semantic model to construct a mapping between elements and attributes between ontology, and eliminated the semantic deviation between user’s need and computer. Finally, the semantic search algorithm was applied to the user’s personalized recommendation system. The experimental results show that the semantic search algorithm effectively improves the search efficiency and reduces the user’s personalized recommendation error.

Key words: ontology mapping, information retrieval, personalized recommendation, extraction rule, semantic search