Research On Query Algorithm Of Information Retrieval

With the rapid development of Internet technology, the information on the Internet increases exponentially. An important research focuses on how to deal with these great capacities of information and acquire relevant data that we need. Techniques for query expansion have been extensively studied in information retrieval research as a means of addressing the word mismatch between queries and documents or defective query expression. These techniques can re-construct or expand the query terms. Researches on query expansion have become hotspot in information retrieval domain, for practical meaning and theoretic importance.Firstly, this paper briefly introduces the development and technology regarding information retrieval. The precision, recall, computational cost and storage requirement about some typical techniques for query expansion are analyzed as well.Secondly, a new method is proposed to solve the problem that local analysis for query expansion gets a low precision in information retrieval. It analyzes top-ranked documents retrieved for an original query, and infers which classifications are related to the query, then utilizes co-occurrence with the query terms in the related classifications to expand the query. Experimental results indicate that the new method gets less retrieval time and higher precision over the global analysis and the local analysis.Thirdly, another new method is proposed to solve the exact matching problem in traditional information retrieval. It uses the proper mnemonic and associational capability of Chaotic Neural Network Model to study and cluster for user query information, track user's interest. Consequently, it speculates on user's polarity, carries through query expansion and re-construction for query terms. Experimental results indicate that the new method gets higher recall and precision than the traditional vector model.Finally, basing on some existing methods, an information retrieval system using the proposed algorithms above is implemented on the basis of query expansion strategies.