Personalized Semantic Query Expansion Based on Dynamic User Query Profile and Spreading Activation Model

Semantic query expansion is a widely used method to resolve the query problems of synonym and polysemy in the information retrieval field. However, it does not make users more satisfied with the search results because too much noise unfit to users’ needs is introduced in the process. In this paper a new framework combining personalization with semantic query expansion is proposed to overcome the noise problem brought by semantic query expansion. In the proposed framework, firstly, instead of using traditional hierarchical expansion strategy, the spreading activation model (SAM) is used for enhancing the selection of expansion terms to reduce the noise. Secondly, to get more accurate expansion terms for individual search, dynamic user query profile is built to capture individual variable query needs and is integrated into the semantic expansion process. The proposed expansion process is described by four steps: building dynamic user query profile, concepts mapping, personalized semantic query expansion and determining the final expansion terms. Four groups of experiments were designed to verify the validity of the proposed method. The experiment results show that the proposed method outperforms both traditional hierarchical expansion and keyword-based query, which manifests that building dynamic user query profile is important for depicting user query needs in semantic query expansion and it is more rational to improve query expansion based on spreading activation model. Moreover, personalized semantic query expansion based on dynamic user query profile and spreading activation model can reduce noise of semantic query expansion and improve the search effectiveness. Keyword: semantic query expansion, personalized information retrieval, dynamic user query profile, spreading activation model