Repeated Rule Acquisition using Rule Ontology from Similar Web Sites Based on Genetic Algorithm.

The Semantic Web, which is the key component of Web 2.0 and Web 3.0, is an evolving development of the World Wide Web in which the semantics of information and services on the Web are being defined. Knowledge is an essential part of most Semantic Web applications and ontology, which is a formal explicit description of concepts or classes in a domain of discussion, is the most important part of the knowledge. As a model for knowledge description and formalization, ontology’s are widely used to represent user profiles in personalized web information gathering. The ontology can decrease the amount of information and reduce the work of utilizing the information in rule acquisition, because it is generalized and specifically rearranged for rule acquisition. Moreover, the ontology can be accumulated and reused throughout repeated rule acquisition. The main contribution of existing work is that the complete and detailed rule composition process with examples and its evaluation. The enhancement work is, with the existing system concept we combining the concept of selecting exact parts that contain rules from Web pages to increase the accuracy in their result. In this proposed work we are combining the screen method from WebPages with use of genetic Techniques to extract the rule optimally.