Research On Web Knowledge Association Mining-based Ontology Evolution

With the proposed concept of the Semantic Web, as a key of the Semantic Web, ontology technology has been widely concerned and studied with a wide application in the knowledge representation, knowledge management, knowledge sharing and knowledge reuse fields. Ontology has been one of the preferred techniques in information knowledge solution. However, the artificial maintenance of ontology is too difficult and too expensive, which greatly limits a broader use and promotion of ontology technology. Although the current ontology evolution made a lot of solutions, it is weaker than mining the association between concepts of ontology and is still not well to solve the problem.Firstly, based on the application requirements of Web information retrieval and analysis system, combined with elements of ontology, the thesis analyzes the target of the evolution of domain ontology and does research on the existing Web mining techniques around the ontology evolution. Secondly, it designs Web knowledge association mining-based ontology evolution programs, divides finished technical solution into two parts, process framework and algorithm for discussion. In the design of process framework, the thesis proposes ontology evolution system and Web information extraction and mining system combined with each other to promote in the process framework and completes ontology evolution in the loop process of the Web information extraction and mining. Binding characteristics of Web information and with the target of more accurate and efficient ontology learning, the thesis improves and designs the specific algorithm in various steps. Finally, according to the designed theoretical framework, it develops a prototype system to verify that the evolution program is steady and effective, solving the difficult problem of artificial maintenance of ontology to some extent and reaching the goal of this study.This thesis has the following innovations: Firstly, this study designs algorithm strategy for Web knowledge association mining to obtain the relationship between the entities and solve the problems that the existing ontology evolution is inadequate for dealing with entity association. Secondly, combining the Web information extraction and mining and ontology evolution, it designs the closed-loop optimization system framework to meet the demand for both the information extraction and mining system and ontology evolution. Finally, aiming at the application characteristics of Web information extraction and mining, it proposes improved algorithm in processing flow and evolved ontology evolution more efficient and more accurate with the algorithm about the relevance of the text judgement based on Bayesian filtering and with the introduction of dynamic content recognition.