Abstract
The growth of unstructured information available inside organizations and on the Web has motivated the building of structures for represent and manipulate such information. Particularly, an ontology provide a structural organizational knowledge to support the exchange and sharing. A crucial element within an ontology is the taxonomy. For building a taxonomy, the identification of hypernymy/hyponymy relations between terms is essential. Lexical patterns have been used in analysis of text for recovering hypernyms. In addition, the Web has been used as source of collective knowledge and it seems a good option for finding appropriate hypernyms. This paper describes an approach to get hypernymy relations between terms belonging to a specific domain knowledge. This approach combines WordNet synsets and context information for building an extended query set. This query set is sent to a web search engine in order to retrieve the most representative hypernym for a term.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gruber, T.: A translation approach to portable ontology specifications. Knowledge Acquisition 5(2), 199–220 (1993)
Caraballo, S.: Automatic construction of a hypernym-labeled noun hierarchy from text. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, pp. 120–126 (1999)
Blohm, S., Cimiano, P.: Learning Patterns from the Web-Evaluating the Evaluation Functions-Extended Abstract. In: OTT 2006, vol. 1, p. 101 (2007)
Snow, R., Jurafsky, D., Ng, A.: Learning syntactic patterns for automatic hypernym discovery. In: Advances in Neural Information Processing Systems, vol. 17, pp. 1297–1304 (2005)
Ortega-Mendoza, R.M., Villaseñor-Pineda, L., Montes-y-Gómez, M.: Using Lexical Patterns for Extracting Hyponyms from the Web. In: Gelbukh, A., Kuri Morales, Á.F. (eds.) MICAI 2007. LNCS (LNAI), vol. 4827, pp. 904–911. Springer, Heidelberg (2007)
Hearst, M.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th Conference on Computational Linguistics, vol. 2, pp. 539–545. Association for Computational Linguistics (1992)
Pantel, P., Ravichandran, D., Hovy, E.: Towards terascale knowledge acquisition. In: Proceedings of the 20th International Conference on Computational Linguistics, p. 771. Association for Computational Linguistics (2004)
Ritter, A., Soderland, S., Etzioni, O.: What is this, anyway: Automatic hypernym discovery. In: Proceedings of AAAI 2009 Spring Symposium on Learning by Reading and Learning to Read, pp. 88–93 (2009)
Kozareva, Z., Hovy, E.: A semi-supervised method to learn and construct taxonomies using the web. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP 2010, pp. 1110–1118. Association for Computational Linguistics, Stroudsburg (2010)
Cimiano, P., Staab, S.: Learning by googling. ACM SIGKDD Explorations Newsletter 6(2), 24–33 (2004)
Sánchez, D.: Domain ontology learning from the web. The Knowledge Engineering Review 24(04), 413–413 (2009)
Turney, P.D., Pantel, P.: From frequency to meaning: vector space models of semantics. J. Artif. Int. Res. 37, 141–188 (2010)
Pantel, P.: Clustering by committee. PhD thesis. University of Alberta (2003)
Sang, E.: Extracting hypernym pairs from the web. In: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, Association for Computational Linguistics, pp. 165–168 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rios-Alvarado, A.B., Lopez-Arevalo, I., Sosa-Sosa, V. (2012). A Taxonomy Construction Approach Supported by Web Content. In: Omatu, S., De Paz Santana, J., González, S., Molina, J., Bernardos, A., Rodríguez, J. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28765-7_55
Download citation
DOI: https://doi.org/10.1007/978-3-642-28765-7_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28764-0
Online ISBN: 978-3-642-28765-7
eBook Packages: EngineeringEngineering (R0)