中文摘要：

查询扩展是提高检索效果的有效方法,传统的查询扩展方法大都以单个查询词的相关性来扩展查询词,没有充分考虑词项之间、文档之间以及查询之间的相关性,使得扩展效果不佳。针对此问题,该文首先通过分别构造词项子空间和文档子空间的Markov网络,用于提取出最大词团和最大文档团,然后根据词团与文档团的映射关系将词团分为文档依赖和非文档依赖词团,并构建基于文档团依赖的Markov网络检索模型做初次检索,从返回的检索结果集合中构造出查询子空间的Markov网络,用于提取出最大查询团,最后,采用迭代的方法计算文档与查询的相关概率,并构建出最终的基于迭代方法的多层Markov网络信息检索模型。实验结果表明：该文的模型能较好地提高检索效果。

英文摘要：

Query expansion is an effective way to improve the retrieval effectiveness,traditional query expansion methods mostly extend the query words only considered the relevance of a single query word,without fully considering the relevance between terms,documents,as well as between queries,so this makes the expansion effect poorly.To solve this problem,first,we construct the Markov network of terms’ and documents’ subspace for extracting the maximum term cliques and document cliques,then,we divide the maximum word cliques into documents dependent word cliques and non-documents dependent word cliques through the mapping relation between term and document cliques,and build the Markov network retrieval model based on document cliques dependency to do the initial search,then we construct the Markov network of queries’ subspace from the search results,which are used for extracting the maximum query cliques,finally,we calculate the probability between document and query in an iterative method,and build the final multi-layer Markov network information retrieval model based on iteration.Experimental results show that our model can improve the retrieval results.