A Protégé Plug-In for Ontology Extraction from Text Based on Linguistic Analysis

  • Paul Buitelaar
  • Daniel Olejnik
  • Michael Sintek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3053)


In this paper we describe a plug-in (OntoLT) for the widely used Protégé ontology development tool that supports the interactive extraction and/or extension of ontologies from text. The OntoLT approach provides an environment for the integration of linguistic analysis in ontology engineering through the definition of mapping rules that map linguistic entities in annotated text collections to concept and attribute candidates (i.e. Protégé classes and slots). The paper ex-plains this approach in more detail and discusses some initial experiments on deriving a shallow ontology for the neurology domain from a corresponding collection of neurological scientific abstracts.


Direct Object Mapping Rule Linguistic Analysis Prepositional Phrase Ontology Engineering 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Agirre, E., Ansa, O., Martinez, D., Hvy, E.: Enriching WordNet concepts with topic signatures. In: Proceedings NAACL WordNet Workshop (2001)Google Scholar
  2. Brants, T.: TnT - A Statistical Part-of-Speech Tagger. In: Proceedings of 6th ANLP Conference, Seattle (2000)Google Scholar
  3. Buitelaar, P.: A Multi-Layered, XML-Based Approach to the Integration of Linguistic and Semantic Annotations. In: Proceedings of EACL 2003 Workshop on Language Technology and the Semantic Web (NLPXML 2003), Budapest, Hungary (April 2003)Google Scholar
  4. Buitelaar, P., Declerck, T.: Linguistic Annotation for the Semantic Web. In: Handschuh, S., Staab, S. (eds.) Annotation for the Semantic Web, IOS Press, Amsterdam (2003)Google Scholar
  5. Buitelaar, P., Olejnik, D., Sintek, M.: OntoLT: A Protégé Plug-In for Ontology Extraction from Text. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, Springer, Heidelberg (2003)Google Scholar
  6. Buitelaar, P., Steffen, D., Volk, M., Widdows, D., Sacaleanu, B., Vintar, Š., Peters, S., Uszko-reit, H.: Evaluation Resources for Concept-based Cross-Lingual Information Retrieval in the Medical Domain. In: Proceedings of LREC 2004 (2004)Google Scholar
  7. Declerck, T.: A set of tools for integrating linguistic and non-linguistic information. In: Proceedings of the SAAKM workshop at ECAI, Lyon (2002)Google Scholar
  8. Deitel, A., Faron, C., Dieng, R.: Learning Ontologies from RDF Annotations. In: Proceedings of the IJCAI Workshop on Ontology Learning, Seattle, Washington (2001)Google Scholar
  9. Faure, D., Nédellec, C., Rouveirol, C.: Acquisition of Semantic Knowledge using Machine learning methods: The System ASIUM. Technical report number ICS-TR-88-16 (1998)Google Scholar
  10. Finn, A., Kushmerick, N.: Active Learning Strategies for Information Extraction. In: Proceed-ings of the ECML/PKDD Workshop on Adaptive Text Extraction and Mining (ATEM), Cavtat-Dubrovnik, Croatia, September 22 (2003)Google Scholar
  11. Gomez-Perez, A., Manzano-Macho, D.: A Survey of Ontology Learning Methods and Tech-niques. Deliverable 1.5, OntoWeb Project (2003)Google Scholar
  12. Gruber, T.: Towards principles for the design of ontologies used for knowledge sharing. Int. Journal of Human and Computer Studies 43(5/6), 907–928 (1994)Google Scholar
  13. Maedche, A.: Ontology Learning for the Semantic Web. The Kluwer International Series in Engineering and Computer Science, vol. 665 (2003)Google Scholar
  14. Maedche, A., Staab, S.: Semi-automatic Engineering of Ontologies from Text. In: Ruhe, G., Bomarius, F. (eds.) SEKE 1999. LNCS, vol. 1756, Springer, Heidelberg (2000)Google Scholar
  15. Miller, G.A.: WordNet: A Lexical Database for English. Communications of the ACM 11 (1995)Google Scholar
  16. Navigli, R., Velardi, P., Gangemi, A.: Ontology Learning and its application to automated termi-nology translation. IEEE Intelligent Systems 18(1) (January/February 2003)Google Scholar
  17. Noy, N.F., Klein, M.: Ontology Evolution: Not the Same as Schema Evolution In: Knowl-edge and Information Systems (in press); Available as technical report SMI-2002-0926 (2002)Google Scholar
  18. Petitpierre, D., Russell, G.: MMORPH - The Multext Morphology Program. Multext deliv-erable report for the task 2.3.1, ISSCO, University of Geneva (1995)Google Scholar
  19. Skut, W., Brants, T.: A Maximum Entropy partial parser for unrestricted text. In: Proceedings of the 6th ACL Workshop on Very Large Corpora (WVLC), Montreal (1998)Google Scholar
  20. Suryanto, H., Compton, P.: Discovery of Ontologies from Knowledge Bases In: Proceedings of the First International Conference on Knowledge Capture, Victoria, BC, Canada (October 2001)Google Scholar
  21. Vossen, P.: EuroWordNet: a multilingual database for information retrieval. In: Proc. of the DELOS workshop on Cross-language Information Retrieval, Zürich, Switzerland, March 5-7 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Paul Buitelaar
    • 1
  • Daniel Olejnik
    • 1
  • Michael Sintek
    • 2
  1. 1.DFKI GmbH, Language TechnologySaarbrueckenGermany
  2. 2.DFKI GmbH, Knowledge ManagementKaiserslauternGermany

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