Purpose of this paper is to resolve the problem of traditional search engines which hardly provide the essential content relevant to the user query. Current techniques for processing of query are mostly based on keywords. Thus, they have limited capabilities to understand the concepts and meaning involved in the user query. The solution for this is a semantic information search. A semantic search overcomes the drawbacks of mismatch associated with traditional keyword-based search. We create a

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... mantic information retrieval system by combining Natural Language Processing and Graph Analysis. The idea of higher-level conceptual understanding of queries is developed to overcome the limitation of the traditional model. We present the method for semantic information retrieval by creating a semantic graph of the query. We then add synonyms and hyponyms of the query terms present in the semantic graph to retrieve the accurate documents. The level of accuracy will be enhanced since the query is analyzed semantically.