基于Markov网络的检索模型

基于Markov网络的信息检索模型提出一种贝叶斯网络推广的检索模型，该模型利用词项在文档集中的共现信息来构造Markov网络，通过该索引项子Markov网络来加载附加查询证据源，计算文档与查询之间的相关性概率，由此概率进行文档排序. 实验结果表明,本文提出的Markov网络模型比其他传统的检索方法具有更优的检索性能.

Information retrieval model based on Markov Network

A novel method is proposed and realized based on Markov Network, which can construct Markov Network according to information of the terms in collection. The relevance probability between document and query is computed by adding additional evidential sources to the model from the term space Markov Network. Experiment results show that our Markov model method can get mote effective performances compared with other methods.