The study of semantic query expansion based on improved vector space model

Semantic-based query expansion is one of the hottest researches in the field of current information retrieval, but it still lacks a better recognized solution. The traditional vector space model succeed in solving the problem of relevance-match between two documents, but it ignores the semantics relevance between the basic language units which constitute the vector. This paper proposes an improved vector space model, which applies in the field of query expansion combined with the Semantic Knowledge Base and gets a good result by testing in the implementation system.