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Traditional search engines like Google and Yahoo fail to rank the relevant information for users' query. This is because such search engines rely on keywords for searching and they fail to consider the semantics of the query. More sophisticated methods that do provide the relevant information for the query is the need of the time. The Semantic Web that stores metadata as ontology could be used to solve this problem. The major drawback of the PageRank algorithm of Google is that ranking is based not only on the page ranks produced but also on the number of hits to the Web page. This paved way for illegitimate means of boosting page ranks. As a result, Web pages whose page rank is zero are also ranked in top-order. This drawback of PageRank algorithm motivated us to contribute to the Web community to provide semantic search results. So we propose ONTOPARK, an ontology based framework for ranking Web pages. The proposed framework combines the Vector Space Model of Information Retrieval with Ontology. The framework constructs semantically annotated Resource Description Framework (RDF) files which form the RDF knowledgebase for each query. The proposed framework has been evaluated by two measures, precision and recall. The proposed framework improves the precision of both single-word and multi-word queries which infer that replacing Web database by semantic knowledgebase will definitely improve the quality of search. The surfing time of the surfers will also be minimized.

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