skip to main content
10.1145/505168.505194acmconferencesArticle/Chapter ViewAbstractPublication PagesfoisConference Proceedingsconference-collections
Article

Using text processing techniques to automatically enrich a domain ontology

Published:17 October 2001Publication History

ABSTRACT

Though the utility of domain Ontologies is now widely acknowledged in an increasing number of domains, several barriers must be overcome before Ontologies become practical and useful tools. A critical issue is the task of identifying, defining, and entering the concept definitions. In case of large and complex application domains this task can be lengthy, costly, and controversial (since different persons may have different points of view about the same concept). To reduce time, cost (and, sometimes, harsh discussions) it is highly advisable to refer, in constructing or updating an ontology, to the documents available in the field. In this paper we describe OntoLearn, a text-mining tool devised to improve human productivity during the process of ontology construction.

References

  1. 1.Agirre E., O. Ausa, E. Havy and D. Martinez "Enriching very large ontologies using the WWW" ECA12000 workshop on Ontology Learning, http:1/012000.aifb.uni-karlsruhe.de/, Berlin, August 2000Google ScholarGoogle Scholar
  2. 2.Basili, R., M.T. Pazienza, P. Velardi, "An Empyrical Symbolic Approach to Natural Language Processing," Artificial Intelligence, 85, 59-99, August 1996 Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. 3.Basili R., M.T. Pazienza F. Zanzotto, "Customizable Modular Lexicalized Parsing Extraction" proc. of Int. Workshop on Parsing Technology, Povo (Trento) February 2000Google ScholarGoogle Scholar
  4. 4.Basili R., M. Missikoff, and P. Velardi (2001) "Identification of relevant terms to support the construction of Domain Ontologies" ACL-0 1 workshop on Human language Technologies, Toulouse, France, July 2001 Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. 5.Brachman R.J. (1979) "On the epistemological status of semantic networks"; in "Associative Networks - Representation and use of Knowledge by Computers," N.V.Findler (Ed.); Academic Press, New York, 1979.Google ScholarGoogle Scholar
  6. 6.Cucchiarelli A., D. Luzi and P. Velardi (1998) "Semantic tagging of Unknown Proper Noun"s in Natural Language Engineering, December 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. 7.Daille B. "Study and Implementation of Combined Techniques for Automatic Extraction of Terminology" Proc. of ACL-94 Workshop "The Balancing Act: combining Symbolic and Statistical Approaches to Language" , New Mexico State University, July 1994.Google ScholarGoogle Scholar
  8. 8.FETISH Groupware (2001) hnp://liss.uni.netlQuick/Place/trial/Main.nsf?OpenDatabaseGoogle ScholarGoogle Scholar
  9. 9.Fano R. "Trasmission of Information, MIT press, 1961Google ScholarGoogle Scholar
  10. 10.Farquhar A., R. Fikes, W. Pratt, J. Rice "Collaborative Ontology Construction for Information Integration" http://www-ksl-svc.stanford.edu:5915/doc/project-papers.htmlGoogle ScholarGoogle Scholar
  11. 11.Jacquemin, C. (1997). "Variation terminologique" Memoire d'Habilitation Diriger des Recherces and Informatique Fondamentale. Universite de Names, Nantes, France.Google ScholarGoogle Scholar
  12. 12.Klavans, J (2001). "Text Mining Techniques for Fully Automatic Glossary Construction," Proceedings of the HTL2001 Conference, San Diego (CA), March, 2001.Google ScholarGoogle Scholar
  13. 13.Miller A. "WordNet: An on-line lexical resource" Special issue of the Journal of Lexicography, 3(4) 1990Google ScholarGoogle Scholar
  14. 14.M. Missikoff, XF. Wang, "Consys - A Web System for Collaborative Ontology Building," submitted, Die. 2000.Google ScholarGoogle Scholar
  15. 15.Maedche B. and S. Staab "Learning Ontologies for the Semantic Web" http://www.aifb.uni-karlsruhe.de/WBS/ama/publications.htmlGoogle ScholarGoogle Scholar
  16. 16.Morin E. and C. Jacquemin "Projecting corpus-based semantic links on a Thesaurus," Proc; of 37th. ACL, 1999 Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. 17.Paliouras V., Cucchiarelli A., Karkaletsis G. Spyropolous C. Velardi P. 'Automatic adaptation of Proper Noun Dictionaries through cooperation of machine learning and probabilistic methods" 23rd annual SIGIR, Athens, June 2000 Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. 18.Pustejovsky J. "The generative lexicon : a theory of computational lexical semantics" MIT press 1993Google ScholarGoogle Scholar
  19. 19.Smadja, F, K. McKeown and V. Hatzivassiloglou (1996) "Translating Collocations for Bilingual Lexicons: a statistical approach," Computational Linguistics, 22: 1 Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. 20.SymOntos (2001), a Symbolic Ontology Management System. http://www.symontos.orgGoogle ScholarGoogle Scholar
  21. 21.Vossen, P. "Extending, Trimming and Fusing WordNet for Technical Documents" NAACL-2001 workshop on WordNet and Other Lexical Resources: Applications, Extensions and Customizations, June 200 1Google ScholarGoogle Scholar
  22. 22.Wagner A. "Enriching a Lexical Semantic Net with Selectional Prefemces by means of Statistical Corpus Analysis" ECA12000 workshop on Ontology Learning, ibidemGoogle ScholarGoogle Scholar
  23. 23.Wilks Y., B. Slator and L. Guthrie "Electric Words: Dictionaries, Computers, and Meaning," MIT Press, Cambridge, MA, 1996 Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Using text processing techniques to automatically enrich a domain ontology

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        FOIS '01: Proceedings of the international conference on Formal Ontology in Information Systems - Volume 2001
        October 2001
        362 pages
        ISBN:1581133774
        DOI:10.1145/505168

        Copyright © 2001 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 17 October 2001

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • Article

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader