Efficient Information Retrieval Using Measures of Semantic Similarity

The semantic information retrieval (IR) is pervading most of the search related vicinity due to relatively low degree of recall or precision obtained from conventional keyword matching techniques. Such techniques miss to retrieve semantically or lexically related terms that are not explicit in the query. In this paper, we present a search engine framework using Google API that expands the user query based on similarity scores of each term of user’s query. We calculated the semantic similarity of noun words to obtain the related concepts described by the search query using WordNet as knowledge source. Users query was replaced with concepts discovered from the similarity measures and fed to the Google search API that resulted in efficient document retrieval.