Query Expansion Optimization Based On Semantic Similarity

At present, the primary reason leading to the poor information query quality is the lack of semantic of search engine tools and searching process. The user's demands submitted are usually simple mechanical syntactic matching, lacking semantic understanding, and the information searched is often missed or false. To solve this problem, the current method is commonly using query expansion techniques. Query expansion refers to putting the user's the synonyms of original query terms submitted as expansion query terms, generating a new query, and then submit to search engines to query.Recently, semantic query expansion technique based on ontology has become a research hotspot. Basically, the essence of Ontological is a description of the relationship between objective reality concepts. Ontology-based query is based on the knowledge, semantic search, the keywords submitted by users through a set of reasoning operation, and synonyms, upper and lower, and lateral expansion, the expansion of the concept of the formation of a rich set of semantics, and finally submitted the query. Therefore, it makes up the traditional keyword search technology based on the defect, resulting in better precision rate guarantee.It is researched ontology modeling and analysis of information search query methods, from the semantic point of view. Aiming at achieving semantic information search technology support, and ultimately improves the retrieval recall.Semantic web model makes a new direction of development for information retrieval. In order to improve precision rate, it is presented an ontology-based information retrieval framework, which is on the basis of some ontology-based information retrieval methods. This retrieval framework uses ontology-based local corpus analytical query extension technology, which bases on the combination of local-corpus-analysis-based query extension methods and ontology technology. Ontology's knowledge expression of domain information makes this new method achieves the support of semantic, and optimizes process of document analysis, as well as improve the analysis efficiency. Under the guarantee of the ontology and local corpus analysis, the accuracy and relevance of the extension result has improved a lot.In order to better improve the efficiency of ontology-based local corpus analytical query extension technology, it is done some optimization to the ontology model, and deal with some horizontal linkages of concepts and entities which eliminate the network-like structure and maintains the tree hierarchy structure in the ontology model. This reduces the complexity of ontology model, as well as makes the ontology structure and relationship of concepts more clear,and similarity calculation more simpler.Finally, using the ontology construction tools to build an ontology instance, an experiment to validate the method of query optimization framework and the effectiveness of relevant search results and show that the proposed ontology-based semantic query expansion technique is more comprehensive, and the relevant search results is much more high, and ultimately increase the precision rate of the paper's expectations.