Research On Techniques Of Web Information Mining For Intelligent Search

The rapid development of Internet has brought the Internet users great convenience to access the information. But the inflation of data make it difficult for users to find valuable information. The development of Search engine caters to the demand of the users in a way. The current Search engines develop towards intelligence , the act of fulfilling depend on a new kind of network technology which is called semantic web. The semantic web technology is the hots pot of the current research of intelligent Search engine,and Semantic Web has combined with Web Mining to form a new research area.We focus our discussion on the research of web information mining of intelligent search in this thesis.Firstly,the background and significance of the subject are introduced simply,the relevant theory of intelligent search is presented. It analyzed and summarized the related theories of semantic web and web mining,explored the way of combining them together.Secondly,based on the work discussed above, we put forward one semantic web knowledge discovery model and illustrated the functions of all subsystems detailly.In addition,it studied on the key techniques of implementation of main fuction module of RDF clustering in the model, and proposed a kind of improved RDFMS(Resource Description Framework Model and Syntax) data hierarchy clustering method based on semantic distances among data. The purpose of this study was to improve the efficiency of reasoning and accuracy in the semantic web. And we reinfored our conclusion by means of real testing examples.In the end,an approach for building a domain ontology reusing WordNet was studied,the experiment shows that WordNet's structure can be reused and domain knowledge is acquired in this way,a domain ontology can be built semi-automatically and quickly.