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.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Fellbaum, C.: A Semantic Network of English: the Mother of all WordNets. Computers and the Humanities 32, 209–220 (1998)
Formica, A., Missikoff, M.: Concept Similarity in SymOntos: an Enterprise Ontology management Tool. The Computer Journal, 583–594 (2002)
Jiang, J.J., Conrath, D.W.: Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. The Computing Research Repository (1997)
Lin, D.: An Information-Theoretic Definition of Similarity. In: Proc. of the Int. Conference on Machine Learning (ICML), pp. 296–304. Morgan Kaufmann (1998)
WordNet 2.1: A lexical database for the English language (2005), http://www.cogsci.princeton.edu/cgi-bin/webwn
Sapkota, K., Thapa, L., Pandey, S.: Efficient Information Retrieval using measures of Semantic Similarity (2006)
Formica, A.: Concept similarity by evaluating information contents and feature vectors: A combined approach. Communications of the ACM 52 (2009)
Resnik, P.: Using Information Content to Evaluate Semantic Similarity in a Taxonomy. In: Proc. 14th Int’l Joint Conf. Artificial Intelligence (1995)
Peng, Q., Zhao, L., Yu, Y., Fang, W.: A new measure of word semantic similarity based on WordNet hierarchy and DAG theory. In: International Conference on Web Information Systems and Mining, doi:10.1109/WISM.2009,44
Resnik, P.: Semantic Similarity in a Taxonomy: An Information-Based Measure and Its Application to Problems of Ambiguity in Natural Language. J. Artificial Intelligence Research 11, 95–130 (1999)
Miller, G.A.: WordNet: A Lexical Database for English. Comm. ACM 38(11), 39–41 (1995)
Miller, G.A., Charles, W.G.: Contextual correlates of semantic similarity. Language and Cognitive Process. 6(1), 1–28 (1991)
Wu, Z., Palmer, M.: Verb semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, pp. 133–138 (1994)
Leacock, C., Chodorow, M.: Combining local context and WordNet similarity for word sense identification. In: Fellbaum 1998, pp. 265–283 (1998)
Wagh, K., Kolhe, S.: Information Retrieval Based on Semantic Similarity Using Information Content. IJCSI International Journal of Computer Science Issues 8(4(2)), 364–370 (2011) ISSN (Online): 1694-0814
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-642-35455-7_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35454-0
Online ISBN: 978-3-642-35455-7
eBook Packages: Computer ScienceComputer Science (R0)