Research On Personalized Intelligent Meta Search Engine

The capacity of information has been increasing massively, since the Internet was invented. However, people urgently need an effective retrieval tool to help them find the right information quickly in the infinite data domain. According to the experts' investigation, the average precision of numerous famous Search Engine systems is below 0.45. Users have to seek help for the other Search engines in order to get the more comprehensive, veracious retrieved information. The arising of the Meta Search engine technique has solved this problem in a sense.The Meta Search engine is an integration Search engine technique, and it is constructed by several single Search Engines. When a meta-engine receives a query from a user, it invokes the underlying search engines to retrieve useful information for the user. The Meta Search engine itself involves three problems: the database selection problem (sub-engines selection), the document selection problem and the result merging problem. If the system policies are designed properly, the meta-engine has high possibility to achieve high coverage, precision and recall. But Meta Search Engine is also confronted with how to analyze personalizing characteristics of information requirements and to provide service with pertinence. If the system integration policy is too simple and there is no mechanism to solve the individualized service, the Meta Search engine would not achieve better effect compare with single search engine.A personalized and intelligent meta search engine is designed in this dissertation in order to improve the insufficiencies faced by traditional meta search engines. Personalization means to set up a user interest model pertinently and to allocate users' queries to their interest domain for the sake of extend query in it. Thus the users' queries can be expressed in a more accurate and clear way. The user interest model can also be applied in result filtering. "Intelligent" means dynamic sub-engines selecting decision on the basis of their performance on particular subjects demanded by the users' queries to some best engines. The research results spread out as follows: 1. Construction of user interest model based on Ontology technologyConstruction of user interest model plays an important role in the performance of Meta search engine. Construction of user interest available is studied in this dissertation. In traditional approaches, word frequency is wildly used to measure user interest and 2-tuple (interest items, interest weight) or 3-tuple (interest items, interest weight, freshness) have been used to expressed user interest model. Interest items are extracted from user's visiting records and interest weight is the arisen probability in the user's visiting records.However, the user interest model constructed only by words may result in the interest domain decentralization. This model can not guide user query pertinently and quite a lot unrelated result may...