The Study Of Ontology-Based Query Expansion

With the rapid development of Internet, the information on it increase rapidly, it's quite difficult for users to look for information in invest information ocean, search engine has become an essential tool in the era of the information explosion. But user's request to information retrieval become higher and higher, so quickness and exactness become the criterion of test information retrieval'performance.Information retrieval is applying in information's represent, store and organization. The information retrieval system was used for managing lots of scientific literature. The paper majorily research query expansion algorithm in IR. There are many query expansion methods, local analysis, and global analysis and so on. This paper presents a innovative query expansion method to improve information retrieval accuracy.This paper research areas and contribution: knowledge ontology was used to depict user's preference to accomplish a Chinese Query Expansion. Analyzing the site maps of the whole user's past browsing via web crawler, then the web with type of pure text is transmitted a new Chinese word segmentation system proposed in this paper, this new algorithm is named N-gram and word-based combined algorithm, constructing a wider range of personalized domain knowledge automatically by Formal Concept Analysis, and combining Query Expansion which is automatic-learning through HowNet, the more complete information can be accessed easily and exactly. When user submits keywords, the system will compare keywords and concepts of personalized ontology in user's profile in order to produce extended keyword sets similar to the keywords inputted and to be recommended to user to acquire more document information including the same concepts. Through the experiments, this method of average F values can reach to 0.83, it's 16.36 percent higher than the traditional query expansion method. It can indeed effectively promote the accuracy of Chinese information retrieval.