Clustered and smarter web mining using semantic web

Semantic Web which is an extension to the current web 2.0 presents information more magnificently for humans and automated tools as well. It offers an intelligent web service that synchronizes and arranges précised data over web in a well-organized modus. The exactitude of fetching relevant data as per the user demand has been a real challenge for traditional web over years in the field of data mining. In this paper, we propose an approach for mapping data over web 3.0 through web ontology and retrieve the précised information through an intelligent agent. The agent provides all the searched data related to the user query from where desired information is found. If the user does not have enough search parameter, mining can be carried out by the information appended by the agent. Our proposed model shortlists the results fetched by traditional search engines such as Google which are semantically related to users´ search parameter. A case study of Institutions of Barisal Division is considered to examine the helpfulness of the model.