Information Retrieval Based on Semantic Similarity Using Information Content

Evaluating semantic similarity of concepts is a problem that has been extensively investigated in the literature in different areas, such as artificial intelligence, cognitive science, databases and software engineering. Semantic similarity relates to computing the similarity between conceptually similar but not necessarily lexically similar terms. Currently, it is growing in importance in different settings, such as digital libraries, heterogeneous databases and in particular the Semantic Web. In such contexts, very often concepts are organized according to taxonomy (or a hierarchy). We investigate approaches to compute the semantic similarity between natural language terms. This paper presents new approach for measuring semantic similarity between words and hierarchical structure is used to present information content. In this paper, we present a search engine using Google API that expands the user query based on similarity scores of each term of user’s query. Users query words are replaced with synonyms discovered from the similarity measures and input to the Google search API.