Traditional web which is the largest information database lacks semantic and as a result the information available in the web is only human understandable, not by machine. With the rapid increase in the amount of information on networks, search engine has become the infrastructure for people gaining access to Web information, and is the second largest Internet application besides e-mail. However, search engine returns a huge number of results, and the relevance between results and user queries is also different. There are lots of search engines available today, but the way to retrieve meaningful information is difficult. To overcome this problem in search engines to retrieve meaningful information intelligently or smartly, Semantic Web technology has played a major role. In the light of this, our paper, proposes an algorithm, architecture for the semantic web based search engine named EWEBSEARCH model, powered by XML meta-tags (which ensures machine understandability) to improve web search. The EWEBSEARCH model provides a simple interface to capture user's queries (keywords), then the search or query engine processes the queries from the repository (database) using the search engine algorithm, interpreting the queries, retrieving and providing appropriate ranking of results in order to satisfy users queries. Query answers are ranked using extended information-retrieval techniques, are generated in an order of ranking and implementation of the model.

Database EWEBSEARCH model Search engine Semantic Web XML meta-tags