Information Retrieval based Improvising Search using Automatic Query Expansion

Current search engines like Google, Yahoo give the search results, indeed users are facing problems in information retrieval. The main problem is because of word mismatch and availability of many resources. Petabytes of information is available because of Internet. From that huge available data, for a naïve user it becomes hectic to distinguish between relevant and irrelevant information to individual interest. Also, another reason of getting irrelevant information is incompatibility between terms that users are using and keywords present in documents. Query expansion is an adding keyword to the original query. The main issue in query expansion is selection of appropriate terms from user's original query. Vocabulary database helps to solve this issue. Identification of the similar words and language entities that are similar in meaning is done by vocabulary which is frequently incorporated in information retrieval system. Thesaurus has been used across a large area of in information retrieval also many applications and natural language processing. In this work, to improve performance of a search query, BM25 model is used for query expansion. Cosine similarity is used to determine similarity between two keywords. Rocchio algorithm is used to calculate the relevance feedback. Experimental result shows better results using Rocchio algorithm.