Abstract
Semantic relatedness measures quantify the degree in which some words or concepts are related, considering not only similarity but any possible semantic relationship among them. Relatedness computation is of great interest in different areas, such as Natural Language Processing, Information Retrieval, or the Semantic Web. Different methods have been proposed in the past; however, current relatedness measures lack some desirable properties for a new generation of Semantic Web applications: maximum coverage, domain independence, and universality.
In this paper, we explore the use of a semantic relatedness measure between words, that uses the Web as knowledge source. This measure exploits the information about frequencies of use provided by existing search engines. Furthermore, taking this measure as basis, we define a new semantic relatedness measure among ontology terms. The proposed measure fulfils the above mentioned desirable properties to be used on the Semantic Web. We have tested extensively this semantic measure to show that it correlates well with human judgment, and helps solving some particular tasks, as word sense disambiguation or ontology matching.
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References
Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American (May 2001)
Bollegala, D., Matsuo, Y., Ishizuka, M.: Measuring semantic similarity between words using web search engines. In: Proc. of WWW 2007, Banff, Canada (2007)
Budanitsky, A., Hirst, G.: Evaluating wordnet-based measures of semantic distance. Computational Linguistics 32(1), 13–47 (2006)
Chen, H.-H., Lin, M.-S., Wei, Y.-C.: Novel association measures using web search with double checking. In: Proceedings of the COLING/ACL 2006, Morristown, NJ, USA. Association for Computational Linguistics (2006)
Cilibrasi, R.L., Vitányi, P.M.: The Google similarity distance. IEEE Transactions on Knowledge and Data Engineering 19(3), 370–383 (2007)
Deerwester, S., Dumais, S., Furnas, G., Landauer, T., Hashman, R.: Indexing by Latent Semantic Indexing. Journal of the American Society for Inf. Science (1990)
Duca, A.: Sketchnet: Knowledge-based word sense disambiguation. In: Proc. of EUROLAN07 Doctoral Consortium (2007)
Euzenat, J., Shvaiko, P.: Ontology matching. Springer, Heidelberg (2007)
Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. In: Proceedings of The Twentieth International Joint Conference for Artificial Intelligence, Hyderabad, India (2007)
Gracia, J., Trillo, R., Espinoza, M., Mena, E.: Querying the web: A multiontology disambiguation method. In: Sixth International Conference on Web Engineering (ICWE 2006), Palo Alto (California, USA). ACM, New York (2006)
Gruber, T.R.: Towards principles for the design of ontologies used for knowledge sharing. In: Formal Ontology. Kluwer, Dordrecht (1993)
Jarmasz, M., Szpakowicz, S.: Roget’s thesaurus and semantic similarity. In: Proceedings of the RANLP-2003, pp. 212–219 (2003)
Karanastasi, A., Christodoulakis, S.: Ontology-driven semantic ranking for natural language disambiguation in the ontonl framework. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 443–457. Springer, Heidelberg (2007)
Kilgarriff, A., Grefenstette, G.: Introduction to the special issue on the web as corpus. Computational Linguistics 29(3), 333–348 (2003)
Miller, G.A., Charles, W.G.: Contextual Correlates of Semantic Similarity. In: Language and Cognitive processes (1991)
Motta, E., Sabou, M.: Next generation semantic web applications. In: 1st Asian Semantic Web Conference. LNCS. Springer, Heidelberg (2006)
Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: 14th International Joint Conference on AI, Montreal (Canada) (1995)
Sabou, M., Gracia, J., Angeletou, S., d’Aquin, M., Motta, E.: Evaluating the semantic web: A task-based approach. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 423–437. Springer, Heidelberg (2007)
Sahami, M., Heilman, T.D.: A web-based kernel function for measuring the similarity of short text snippets. In: Proc. of the 15th Int. WWW Conference (2006)
Sheth, A., Arpinar, I., Kashyap, V.: Relationships at the Heart of Semantic Web: Modeling, Discovering and Exploiting Complex Semantic Relationships, vol. 139. Springer, Heidelberg (2003)
Strube, M., Ponzetto, S.P.: Wikirelate! computing semantic relatedness using Wikipedia. In: AAAI. AAAI Press, Menlo Park (2006)
Ted Pedersen, S.P., Banerjee, S.: Maximizing semantic relatedness to perform word sense disambiguation. Research Report UMSI 2005/25 (2005)
Trillo, R., Gracia, J., Espinoza, M., Mena, E.: Discovering the semantics of user keywords. Journal on Universal Computer Science (JUCS). Special Issue: Ontologies and their Applications 13(12), 1908–1935 (2007)
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Gracia, J., Mena, E. (2008). Web-Based Measure of Semantic Relatedness. In: Bailey, J., Maier, D., Schewe, KD., Thalheim, B., Wang, X.S. (eds) Web Information Systems Engineering - WISE 2008. WISE 2008. Lecture Notes in Computer Science, vol 5175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85481-4_12
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DOI: https://doi.org/10.1007/978-3-540-85481-4_12
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