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Remarkable growth in the electronics and communication field provides ubiquitous services. It also permits to save huge amount of documents on web. As a result, it is very difficult to search a specific and desired information over the Internet. Classical search engines were unable to investigate the content on web intelligently. The tradition searching results has a lot of immaterial information along with desired one as per user query. To overcome from stated problem many modifications are done in traditional search engines to make them intelligent. These search engines are able to analyze the stored data and reflects only appropriate contents as per users query. Semantic Web is an emerging and efficient approach to handle the searching queries. It gathers appropriate information from web pool based on logical reasoning. It also incorporates rulebased system. Semantic web reasonably scrutinizes webs contents using ontology. The learning process of ontology not only intelligently analyze the contents on web but also improves scrutinizing process of search engine. The paper suggests a new keyword-based semantic retrieval scheme for google search engines. The schemes accelerates the performance of searching process considerably with the help of domain-specific knowledge extraction process along with inference and rules. For this, in ontology the prefix keywords and its sematic association are pre-stored. The proposed framework accelerates the efficiency of content searching of google search engine without any additional burden of end users.

An Efficient and Optimized Sematic Web Enabled Framework (EOSWEF) for Google Search Engine Using Ontology