Ontology-Based Web Information Retrieval

With the development of Internet, the web information Retrieval has become the most popular method to obtain information. Therefore How to improve the ability of web information retrieval has become an important research topic.In this paper, the following work has been done on web information retrieval:The thesis proceed with analyzing webpages' structure based on their feature and propose a method named Webpage Noise Filtering(WNF). WNF can eliminate HTML tag, copyright information and most ads of a webpage. Experiments in this thesis show a good efficiency. In the way of webpage theme extraction, the paper use DOM tree parsing and propose container tree method. Experiments shows that the extraction methods can efectively eliminate contents irrelative with webpages theme and reserve webpages' theme and relative information.Semantic-based is an effective way to improve the ability of web information retrieval.Because of ontology's excellent hierarchical concept structure, logic support and the ability to express semantics, ontology-based information retrieval become an important research topic. This thesis summarizes the domestic and international theory on research of ontology based information retrieval. Based construction of domain ontology, attempts the research of domain ontology based semantic text information retrieval.Propose and implemente an ontology-based semantic retrival model.In order to overcome the shotcomings of tradetional vector space model in dealing with items, materializtion of semantic relations between keywords, and the similarity between semantic vectors, and so on. This method improves the performance of the Information Retrieval system.Resarch on semantic information retrieval is of important theoretical value and widely used in search engine area. This dissertation has done some research on its modeling and application. The emphasis of our further research will be on the application, evalueation, and employment of the semantic information retrieval method to the web search engine.