Abstract
Automated construction of ontologies from text corpora, which saves both time and human effort, is a principal condition for realizing the idea of the Semantic Web. However, the recently proposed automated techniques are still limited in the scope of context that can be captured. Moreover, the source corpora generally lack the consensus of ontology users regarding the understanding and interpretation of ontology concepts. In this paper we introduce an unsupervised method for learning domain n-ary relations from Wikipedia articles, thus harvesting the consensus reached by the largest world community engaged in collecting and classifying knowledge. Providing ontologies with n-ary relations instead of the standard binary relations built on the subject-verb-object paradigm results in preserving the initial context of time, space, cause, reason or quantity that otherwise would be lost irreversibly. Our preliminary experiments with a prototype software tool show highly satisfactory results when extracting ternary and quaternary relations, as well as the traditional binary ones.
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References
Banko, M., Etzioni, O.: The Tradeoffs between Open and Traditional Relation Extraction. Proc. Assoc. Comp. Linguistic (2008)
Buitelaar, P., Cimiano, P. (eds.): Ontology Learning and Population: Bridging the Gap between Text and Knowledge - Selected Contributions to Ontology Learning and Population from Text. IOS Press, Amsterdam (2008)
Ciaramita, M., Gangemi, A., Ratsch, E., Šarić, J., Rojas, I.: Unsupervised Learning of Semantic Relations for Molecular Biology Ontologies. In: [2]
Cimiano, P.: Ontology Learning and Population from Text: Algorithms, Evaluation and Applications. Springer, Heidelberg (2006)
Eichler, K., Hemsen, H., Neumann, G.: Unsupervised Language Extraction from Web Documents. In: Proc. LREC 2008, pp. 1674–1679 (2008)
Fellbaum, C. (ed.): WordNet. An Electronic Lexical Database. MIT Press, Cambridge (1998)
Hepp, M., Bachlechner, D., Siorpaes, K.: Harvesting Wiki Consensus - Using Wikipedia Entries as Ontology Elements. In: Workshop Semantic Wikis (2006)
Kavalec, M., Svátek, V.: A study on automated relation labelling in ontology learning. In: Buitelaar, P., Cimiano, P., Magnini, B. (eds.) Ontology learning from text: methods, evaluation and applications. IOS Press, Amsterdam (2005)
de Marneffe, C.-M., MacCartney, B., Manning, C.D.: Generating Typed Dependency Parses from Phrase Structure Parses. In: Proc. LREC 2006, pp. 449–454 (2006)
Milne, D.N., Witten, I.H.: Learning to link with Wikipedia. In: CIKM, pp. 509–518 (2008)
Pantel, P., Pennacchiotti, M.: Automatically Harvesting and Ontologizing Semantic Relations. In: [2]
Sánchez, D., Moreno, A.: Learning non-taxonomic relationships from web documents for domain ontology construction. Data Knowl. Eng. 64(3), 600–623 (2008)
Suchanek, F.M., Kasneci, M., Weikum, G.: Yago: a Core of Semantic Knowledge. In: Proc. WWW 2007, pp. 697–706 (2007)
W3C: Defining N-ary Relations on the Semantic Web. W3C Working Group Note (2006), http://www.w3.org/TR/2006/NOTE-swbp-n-aryRelations-20060412/
W3C: OWL Web Ontology Language. W3C Recommendation (2004), http://www.w3.org/TR/2004/REC-owl-features-20040210/
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Banek, M., Jurić, D., Skočir, Z. (2010). Learning Semantic N-Ary Relations from Wikipedia. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds) Database and Expert Systems Applications. DEXA 2010. Lecture Notes in Computer Science, vol 6261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15364-8_39
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DOI: https://doi.org/10.1007/978-3-642-15364-8_39
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