关键词: 相似性搜索, 用户导向预测, 语义预测, 异构网络

Abstract: With the increasing of information on heterogeneous network, how to help users get the needed information accurately has become a hot issue. Similarity search has been widely used in web search. The similarity search based on meta-path can express the semantics contained in heterogeneous network better. However the number of selected paths is large when use such kind of methods, which leads to large scale of computation. To address above problems, an important research area, similarity search that uses user-guide to predict the meta-paths, grows up. This kinds of approaches require the user to provide a sample of results as a guide while querying, which will be used to predict the meta-paths associated with user queries. By now, research for predict related meta-path which use the user-guide information mainly aimed at the same type of node in heterogeneous network. In this paper, the structural information of the heterogeneous network graph is used to establish the semantic prediction model, calculate the probability of the candidate paths that match user’s search, and then select the path with the highest probability. Experimental results show that the proposed algorithm can predict the relationship between the same type and different types of nodes in heterogeneous network. Meanwhile, it has good performance and effectiveness. This paper provides a concrete implemental method for how to obtain similarity results in multi-semantic environment.

Key words: similarity search, user-guided prediction, semantic prediction, heterogeneous network