Abstract: The weight of term has been widely used in models of information retrieved. In order to solve the problem of independence assumption of word bags mode for traditional model, the weight of term based on the importance of term will be used in the Markov network query expansion model. In order to calculate the weight of the term, firstly we must establish the graph-of-word of documents. Then according to the graph-of-word, we get the matrix that terms occur together and the probability transfer matrix between terms. Lastly, we use the chain of Markov to get the weight of term. By putting the weight of term into the Markov network query expansion model, the experiment results on 5 standard datasets show that the search results of using Markov network query expansion model based on term importance are better than those based on traditional model of word bags.

Key words: graph-of-word, Markov network, query expansion