Web-Based Measure of Semantic Relatedness
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.
Keywords
semantic web relatedness relationship discoveringPreview
Unable to display preview. Download preview PDF.
References
- 1.Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American (May 2001)Google Scholar
- 2.Bollegala, D., Matsuo, Y., Ishizuka, M.: Measuring semantic similarity between words using web search engines. In: Proc. of WWW 2007, Banff, Canada (2007)Google Scholar
- 3.Budanitsky, A., Hirst, G.: Evaluating wordnet-based measures of semantic distance. Computational Linguistics 32(1), 13–47 (2006)CrossRefGoogle Scholar
- 4.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)Google Scholar
- 5.Cilibrasi, R.L., Vitányi, P.M.: The Google similarity distance. IEEE Transactions on Knowledge and Data Engineering 19(3), 370–383 (2007)CrossRefGoogle Scholar
- 6.Deerwester, S., Dumais, S., Furnas, G., Landauer, T., Hashman, R.: Indexing by Latent Semantic Indexing. Journal of the American Society for Inf. Science (1990)Google Scholar
- 7.Duca, A.: Sketchnet: Knowledge-based word sense disambiguation. In: Proc. of EUROLAN07 Doctoral Consortium (2007)Google Scholar
- 8.Euzenat, J., Shvaiko, P.: Ontology matching. Springer, Heidelberg (2007)MATHGoogle Scholar
- 9.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)Google Scholar
- 10.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)Google Scholar
- 11.Gruber, T.R.: Towards principles for the design of ontologies used for knowledge sharing. In: Formal Ontology. Kluwer, Dordrecht (1993)Google Scholar
- 12.Jarmasz, M., Szpakowicz, S.: Roget’s thesaurus and semantic similarity. In: Proceedings of the RANLP-2003, pp. 212–219 (2003)Google Scholar
- 13.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)CrossRefGoogle Scholar
- 14.Kilgarriff, A., Grefenstette, G.: Introduction to the special issue on the web as corpus. Computational Linguistics 29(3), 333–348 (2003)CrossRefMathSciNetGoogle Scholar
- 15.Miller, G.A., Charles, W.G.: Contextual Correlates of Semantic Similarity. In: Language and Cognitive processes (1991)Google Scholar
- 16.Motta, E., Sabou, M.: Next generation semantic web applications. In: 1st Asian Semantic Web Conference. LNCS. Springer, Heidelberg (2006)Google Scholar
- 17.Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: 14th International Joint Conference on AI, Montreal (Canada) (1995)Google Scholar
- 18.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)CrossRefGoogle Scholar
- 19.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)Google Scholar
- 20.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)Google Scholar
- 21.Strube, M., Ponzetto, S.P.: Wikirelate! computing semantic relatedness using Wikipedia. In: AAAI. AAAI Press, Menlo Park (2006)Google Scholar
- 22.Ted Pedersen, S.P., Banerjee, S.: Maximizing semantic relatedness to perform word sense disambiguation. Research Report UMSI 2005/25 (2005)Google Scholar
- 23.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)Google Scholar