| These years, because of the brevity and semantic ambiguity of user query words, most search engines face a problem to understand the meaning of query words。How topic search engine to not only accurately understand user submiting information needs, but also possess of the relevant semantic knowledge of query information source, and how to automatically and distinguishingly return the accurate relevant information to each user when"different users enter the same query keywords"and"the same user inputs different query keywords"to topic search engine, which is our main research issues. Most search engines gather a large number of user query logs, which record the user history queries and clicks on information, and reflect the user's interest and domain knowledge to varying degrees. More users record, more accurate to characterize the user's domain knowledge. Ontology has a good concept structure and support for logical reasoning, owns the ability of expression semantics based on the relationship of concepts, and also can provide basic knowledges for semantic search and concept search. WordNet contains a large mumber of queries relations , such as"synonym","synonyms","isa"and"part of", which can reflect expert's knowledges. Therefore, to take use of rich user query logs and semantic relations in WordNet to construct ontology as semantic backgroud of topic search engine, it provide a vast world for developing a new generation of Personalized Topic information retrieval system。Studing the relations of user query words and web clicks in history knowledge records, and constructing the model of semantic relations which reflects the user personalized knowledge between user query words, has become particularly important.The main contents of this paper are summarized as follows:First, we present a new method of personalized user-query semantic clustering to classify user query words into subjects by user's personal interests and background knowledge. User query logs contain a wealth of user-access history records, these records reflect user interests and domain knowledge to some extend. Above all, we propose three semantic relations based on user query logs, such as based on the query word itself ,based on user query click sequence and based on user query click content. Then, according to the analysis of these three semantic relati ons, we propose a novel computing method of user query semantic similarity. Based on this user query semantic similarity ,we can get the function of cluster similarity, and by hiera- rchical agglomerative clustering algorithm, we can cluster user query terms into semantic subjects based on the reflected topics in user query logs so as to disambiguated the semantic ambiguity of user query words.Secondly, we propose a method to construct user-query semantic ontology (UQSO) which is a model of user query interest domain knowledge in use of user query semantic clustering and queries relations in WordNet. UQSO describes user interest domian knowledge and formes the basis of personalized topic search engine. This ontology express the user interest preferences, and then based on this to establish user group and group preferences which if is applied to search engines, will improve the technical level of information collection from based on similarity matching of keywords to based on semantic query, and which is convenient for users to provide more suitable information, thus achieve the purpose of personalized search.Finally, we use Porotégé2000 ontology construction tools, and VC++ programming language for the experimental verification to cluster a user query word set, and take use of WordNet to build user-query semantic ontology (UQSO). Our experiment shows that, by this ontology construction method, the true meaing of user query words can be better distinguished according to the user interests and background knowledges, and query semantic ambiguity can be eliminated. Therefor UQSO can be a foundation of the realization of personalized topic search. |
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