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Most of the Information Retrieval techniques are based on representing the documents using the traditional vector space model i.e. bag-of-words model. In this paper, associations among words in the documents are assessed and it is expressed in term graph model to represent the document content and the relationship among the keywords. Most modern web search engines typically employ two-level ranking strategy. Firstly, an initial list of documents is prepared using a low-quality ranking function with consumes less computation. Secondly, initial list is then re-ranked by machine learning algorithms which involve expensive computation. This paper experiments the second level of ranking strategy which exploits term graph data structure to assess the importance of a document for the user query and thus documents are re-ranked according to the association and similarity exists among the documents. The proposed algorithms achieve promising results within the top 10 search results.

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