Semantic query expansion method based on pay-as-yougo fashion for graph model

Abstract

Abstract The search requirements of users are usually vague and unclear, and the traditional keyword query method is difficult to obtain satisfactory query results. Therefore, the semantic query expansion technology uses semantic information to modify and extend the initial search requirements of users, to obtain more comprehensive and accurate query results. However, the construction cost of the extended query graph is high, and the existing research is difficult to adjust and optimize the user’s query demand in time, resulting in a poor query effect. To solve the above problems, this paper proposes a semantic expansion method based on pay-as-you-go fashion for graph model: Firstly, the initial query graph is constructed according to the user’s search requirements, and the semantic similarities between the search requirements and knowledge bases are calculated; Then, the extended sets of attribute-values and edges are generate by sorting the similarity value in descending order; Finally, all or part of the elements in the extended set are combined to generate the extended query graph. This method can dynamically adjust and optimize the initial query based on the relevant semantic information in time, and improve the query efficiency and effect.