Abstract
Word similarity assessment is one of the most important elements in Natural Language Processing (NLP) and information retrieval. 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, its importance is growing in different settings, such as digital libraries, heterogeneous databases and in particular the Semantic Web. In this paper, authors present a search engine framework using Google API that expands the user query based on similarity scores of each term of user’s query. The authors calculated the semantic similarity of noun words to obtain the related concepts described by the search query using WordNet. Users query is replaced with concepts discovered from the similarity measures. Authors present a new approach to compute the semantic similarity between words. A common data set of word pairs is used to evaluate the proposed approach: first calculate the semantic similarities of 30 word pairs, then the correlation coefficient between human judgement and three computational measures are calculated, the experimental result shows new approach is better than other existing computational models.
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Wagh, K., Kolhe, S. (2012). A New Approach for Measuring Semantic Similarity in Ontology and Its Application in Information Retrieval. In: Sombattheera, C., Loi, N.K., Wankar, R., Quan, T. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2012. Lecture Notes in Computer Science(), vol 7694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35455-7_12
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DOI: https://doi.org/10.1007/978-3-642-35455-7_12
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