中文摘要：

全局分析方法是一种常用而能有效改善信息检索效果的查询扩展方法。通过计算词间相似度构造Markov网络模型：然后由此模型加强候选词集中的词相关性描述，并提取了在Markov网络中词间的团结构；通过在查询中加入查询词所在团中的其他候选词进行查询扩展。实验表明基于Markov网络团的信息检索模型的检索效果优于基于一般的相似性矩阵查询扩展的检索效果；基于团提取方法的查询扩展的检索效果优于普通的基于提取方法的查询扩展检索效果。

英文摘要：

Query expansion based on global analysis model is a common and effective approach to improve information retrieval performance. First, the Markov network model was built by calculating the similarity between terms. Second, the description of relationship between candidate terms was strengthened, and the clique structure was extracted from the Markov network. Finally, candidate terms and query terms in the clique structure were merged for query expansion. Ex- perimental results showed that query expansion based on the Markov random walk matrix performs better than query ex- pansion based on the similarity matrix, and query expansion based on the clique extraction method performs better than query expansion based on the general extraction method.