STRUCTURING TAXONOMIES BY USING LINGUISTIC PATTERNS AND WORDNET ON WEB SEARCH
Ana B. Rios-Alvarado, Ivan Lopez-Arevalo, Victor Sosa-Sosa
2011
Abstract
Finding an appropriate structure for representing the information contained in texts is not a trivial task. Ontologies provide a structural organizational knowledge to support the exchange and sharing of information. A crucial element within an ontology is the taxonomy. For building a taxonomy, the identification of hypernymy/hyponymy relations between terms is essential. Previous work have used specific lexical patterns or they have focused on identifying new patterns. Recently, the use of theWeb as source of collective knowledge seems a good option for finding appropriate hypernyms. This paper introduces an approach to find hypernymy relations between terms belonging to a specific knowledge domain. This approach combinesWordNet 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.
References
- Blohm, S. and Cimiano, P. (2007). Learning Patterns from the Web-Evaluating the Evaluation FunctionsExtended Abstract. OTT'06, 1:101.
- Burgun, A. and Bodenreider, O. (2001). Aspects of the taxonomic relation in the biomedical domain. In Proceedings of the international conference on Formal Ontology in Information Systems-Volume 2001, page 233. ACM.
- Caraballo, S. (1999). Automatic construction of a hypernym-labeled noun hierarchy from text. In Proceedings of the 37th annual meeting of the Association for Computational Linguistics, pages 120-126. Association for Computational Linguistics.
- Cederberg, S. and Widdows, D. (2003). Using LSA and noun coordination information to improve the precision and recall of automatic hyponymy extraction. In Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003-Volume 4, page 118. Association for Computational Linguistics.
- Cicurel, L., Bloehdorn, S., and Cimiano, P. (2007). Clustering of polysemic words. Advances in Data Analysis, pages 595-602.
- Cimiano, P. and Staab, S. (2004). Learning by googling. ACM SIGKDD explorations newsletter, 6(2):24-33.
- Gruber, T. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2):199- 220.
- Hearst, M. (1992). Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th conference on Computational linguistics-Volume 2, pages 539-545. Association for Computational Linguistics.
- Maedche, A. and Staab, S. (2001). Ontology learning for the semantic web. Intelligent Systems, IEEE, 16(2):72-79.
- Ortega-Mendoza, R., Villasen˜or-Pineda, L., and y Gómez, M. M. (2007). Using lexical patterns for extracting hyponyms from the web. MICAI 2007: Advances in Artificial Intelligence, pages 904-911.
- Pantel, P. (2003). Clustering by committee. PhD thesis, University of Alberta.
- Pantel, P. and Lin, D. (2002). Discovering word senses from text. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 613-619. ACM.
- Pantel, P., Ravichandran, D., and Hovy, E. (2004). Towards terascale knowledge acquisition. In Proceedings of the 20th international conference on Computational Linguistics, page 771. Association for Computational Linguistics.
- Ritter, A., Soderland, S., and Etzioni, O. (2009). What is this, anyway: Automatic hypernym discovery. In Proceedings of AAAI-09 Spring Symposium on Learning by Reading and Learning to Read, pages 88-93.
- Sánchez, D. (2009). Domain ontology learning from the web. The Knowledge Engineering Review, 24(04):413-413.
- Sang, E. (2007). Extracting hypernym pairs from the web. In Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions, pages 165-168. Association for Computational Linguistics.
- Schutz, A. and Buitelaar, P. (2005). Relext: A tool for relation extraction from text in ontology extension. In Gil, Y., Motta, E., Benjamins, V., and Musen, M., editors, The Semantic Web ISWC 2005, volume 3729 of Lecture Notes in Computer Science, pages 593-606. Springer Berlin / Heidelberg.
- Snow, R., Jurafsky, D., and Ng, A. (2005). Learning syntactic patterns for automatic hypernym discovery. Advances in Neural Information Processing Systems, 17:1297-1304.
- Turney, P. D. and Pantel, P. (2010). From frequency to meaning: vector space models of semantics. J. Artif. Int. Res., 37:141-188.
Paper Citation
in Harvard Style
B. Rios-Alvarado A., Lopez-Arevalo I. and Sosa-Sosa V. (2011). STRUCTURING TAXONOMIES BY USING LINGUISTIC PATTERNS AND WORDNET ON WEB SEARCH . In Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2011) ISBN 978-989-8425-80-5, pages 273-278. DOI: 10.5220/0003665902730278
in Bibtex Style
@conference{keod11,
author={Ana B. Rios-Alvarado and Ivan Lopez-Arevalo and Victor Sosa-Sosa},
title={STRUCTURING TAXONOMIES BY USING LINGUISTIC PATTERNS AND WORDNET ON WEB SEARCH},
booktitle={Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2011)},
year={2011},
pages={273-278},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003665902730278},
isbn={978-989-8425-80-5},
}
in EndNote Style
TY - CONF
JO - Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2011)
TI - STRUCTURING TAXONOMIES BY USING LINGUISTIC PATTERNS AND WORDNET ON WEB SEARCH
SN - 978-989-8425-80-5
AU - B. Rios-Alvarado A.
AU - Lopez-Arevalo I.
AU - Sosa-Sosa V.
PY - 2011
SP - 273
EP - 278
DO - 10.5220/0003665902730278