A semantic enabled intelligent search system for educational information resources

Abstract

Aiming at the special requirements oriented to the education field, this paper proposes an intelligent search system based on semantic web. Under the guidance of the users' needs and the proposed Education Information Intelligent Search (EIIS) framework, the EIISReasoning mechanism and its relevant rule sets are constructed to automatically reason the search concepts in order to self-adapt semantics to more levels, where the semantic factor SR and the response time factor TR are both introduced to detail field application requirements. After this, the EIISRanking algorithm applies two levels ranking by the weight factor of semantic similarity, and the same relevance is differentiated in terms of education field needs. The case test shows that the semantic based intelligent search system takes on a better precision rate, and the ranking algorithm can improve execution performance.