Automatic extraction of semantic relationships for WordNet by means of pattern learning from Wikipedia

Ruiz-Casado, María; Alfonseca Cubero, Enrique; Castells Azpilicueta, Pablo

UAM_Biblioteca

Author

Ruiz-Casado, María; Alfonseca Cubero, Enrique; Castells Azpilicueta, Pablo

Entity

UAM. Departamento de Ingeniería Informática

Publisher

Springer-Verlag Berlin Heidelberg

Date

2005

Citation

Natural Language Processing and Information Systems: 10th International Conference on Applications of Natural Language to Information Systems, NLDB 2005, Alicante, Spain, June 15-17, 2005. Proceedings. Lecture Notes in Computer Science, Volumen 3513. Springer 2005. 67-79.

ISSN

0302-9743 (print); 1611-3349 (online)

ISBN

978-3-540-26031-8 (print); 978-3-540-32110-1 (online)

DOI

10.1007/11428817_7

Funded by

This work has been sponsored by CICYT, project number TIC2002-01948.

Editor's Version

http://dx.doi.org/10.1007/11428817_7

Subjects

Database Management; Computer Communication Networks; Logics and Meanings of Programs; Information Storage and Retrieval; Artificial Intelligence; Mathematical Logic; Formal Languages; Informática

URI

http://hdl.handle.net/10486/664305

Note

The final publication is available at Springer via http://dx.doi.org/10.1007/11428817_7
Proceedings of 10th International Conference on Applications of Natural Language to Information Systems, NLDB 2005, Alicante, Spain, June 15-17, 2005.

Rights

Abstract

This paper describes an automatic approach to identify lexical patterns which represent semantic relationships between concepts, from an on-line encyclopedia. Next, these patterns can be applied to extend existing ontologies or semantic networks with new relations. The experiments have been performed with the Simple English Wikipedia and WordNet 1.7. A new algorithm has been devised for automatically generalising the lexical patterns found in the encyclopedia entries. We have found general patterns for the hyperonymy, hyponymy, holonymy and meronymy relations and, using them, we have extracted more than 1200 new relationships that did not appear in WordNet originally. The precision of these relationships ranges between 0.61 and 0.69, depending on the relation.

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