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.
Keywords: Search engine, Concept, Information content similarity
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