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Traditional information retrieval systems lack consistent semantic description of information i.e. they fail to meet users' need due to lack of applying semantic identification to extract the information from the available information. Use of semantic equivalent of the user query will improve the efficiency of the search. In this paper, we propose a framework for semantic based information retrieval. Here we find the concepts that user specify in their query by analyzing the semantic equivalencies. The result which is a set of alternate queries to the main search query is then compared with the existing keyword based system's result. Then, according to the alternate queries' search results, the main queries result gets rearranged by assigning new weights. We further personalize the search and then re-rank the results on user preference. The proposed semantic retrieval model is combined with keyword based model to achieve completeness of the knowledge base. The model which we propose is helping to project the most relevant result URLs to the higher ranks.