关键词: 层次依赖, Markov网络, 查询扩展, 信息检索

Abstract: Query expansion is one of key technologies to solve the low efficiency problem which is caused by the term mismatch between user query and relevant documents. This paper proposes a Markov network information retrieval expanded model based on hierarchical dependence. This model considers these factors comprehensively such as hierarchy distance between candidates and query terms, relevance between terms, the out degree of a term and path selection. This model also helps to mine more potential candidates by term reweighting with hierarchical dependence and to select candidates with more relevant to query for information retrieval expanded model. The experimental results on five standard collections demonstrate that the Markov network information retrieval expanded model based on hierarchical dependence outperforms BM25 model without query expansion by 5%-41% and 5%-70% in 3-avg and 11-avg respectively. Compared with the Markov network information retrieval expanded model based on direct correlation, the proposed model performs better overall on retrieval efficiency.

Key words: hierarchical dependence, Markov network, query expansion, information retrieval