There are trillions of web pages available on the World Wide Web for the user queries to give their search results. However, these results may be relevant to the user queries or they can be irrelevant to the queries. As a solution to this, we are proposing a personalized search engine which will provide the more relevant search results to the user queries. In order to do so, we will be using an ontology concept, according to this we are going to split the user query into two parts: content and

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... ocation. The location and content concepts will be used to build an ontology profile which in then will be used to capture user interests. Using user queries the feature vectors will be extracted and which in turn will be trained using re-ranking algorithm to rank the future results. We perform an extensive query search experiments to compare the effectiveness of proposed and existing systems.