Research Of Semantic Query Expansion Based On Concept Semantic Space

In the development of Internet and information technology throughout thenational society, vast information emerges to us. The requirement of getting more andmore accurate information accelerates development of information retrievaltechnology. Current popular Search Engines still run with the method of searching keywords which provides us a simple search platform while getting a lot of uselessinformation that irrelevant to customers’ willing. Therefore, many academicians areusing a method called Query Expansion to resolve the problem of retrieval information.Query Expansion adds relevant words or phrases into original query in order toovercome ambiguous problem in natural language and describe customers’ willingmore clearly. Although traditional query expansion which only expands symbol andcharacter string taking query words as the center has been improved frequently, itignores semantic relationship in queries and fails to express customers’ willingcorrectly.In recent years, semantic concept query expansion has become to a new focus. Itmeans to construct concept semantic space on the basis of semantic dictionary orontology, and then extract meaning and relationship of query words. The way of queryexpansion achieves expansion in semantic aspect to a degree but results in querydrifting frequently because it depends on complete semantic construction and manyirrelevant expansion words are added into query collection.In view of current problems in query expansion, this task designs to queryexpansion in meaning making use of semantic dictionary and documents, introducingthe advantage of semantic analysis and statistical model on the basis of achievementsof predecessors. Therefore, the keys to this study are as follow: 1. Semantic dictionary and ontology have become necessary tools in intelligentinformation retrieval. On the basis of traditional method, we take advantage of findingcommon ancestor in order to structure overall, compact, efficient Concept SemanticSpace.2. In the process of controlling number of expansion terms, this task Importsdynamic observation windows weighting model into the algorithm, so that enhance thefunction of co-occurrence information in relevancy between every two words to getfinal expansion words by setting threshold about relational degree dynamically.3. Finally, authors develop an experiment system using testing data set providedby Text REtrieval Conference and evaluate the results of experiment by MRR. Theexperimental results show that the expansion algorithm of this task is much moreeffective than traditional pseudo-relevance feedback algorithm.All the experimental data are from Federal Register part of TREC which is395MB totally. They include56110chapters,50query topics and50target numbers ofchapters. These data are very standard from TREC, so that they ensure experimentalobjectivity.