Abstract:

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. Experimental results showed that query expansion based on the Markov random walk matrix performs better than  query expansion 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.

Key words:  query expansion; global analysis; Markov network; clique