Language Technologies Meet Ontology Acquisition

  • Galia Angelova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3596)

Abstract

This paper overviews and analyses the on-going research attempts to apply language technologies to automatic ontology acquisition. At first glance there are many successful approaches in this very hot field. However, most of them aim at the extraction of named entities as well as draft taxonomies and partonomies. Only few attempts exist for enriching ontologies by applying word-sense disambiguation. There are principle obstacles to extract automatically coherent conceptualisations from raw texts: it is impossible to identify exactly the types and their instances as well as the word meanings which denote types. It is also impossible to validate a text-based conceptual model against the real world. Thus we can expect only partial success in the semi-automatic acquisition in specific (limited) domains, by workbenches supporting the human knowledge engineer in the final ontological choices.

Keywords

natural language processing information extraction automatic knowledge acquisition from text 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Berners-Lee, T.: Weaving the Web. Harper (1999)Google Scholar
  2. 2.
    Reinberger, M.-L., Spyns, P., Pretorius, A.J., Daelemans, W.: Automatic Initiation of an Ontology. In: Meersman, R., Tari, Z. (eds.) OTM 2004. LNCS, vol. 3290, pp. 600–617. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Hirschman, L.: The Evolution of Evaluation: Lessons from the Message Understanding Conferences. Computer Speech and Language 12, 281–305 (1998)CrossRefGoogle Scholar
  4. 4.
    Popov, B., Kiryakov, A., Ognyanoff, D., Manov, D., Kirilov, A.: KIM – a Semantic Platform for Information Extraction and Retrieval. Journal of Natural Language Engineering 10(3/4), 375–392 (2004); see also: http://62.213.161.156/KIM/screen/KWUIMain.jsp CrossRefGoogle Scholar
  5. 5.
    Handschuh, S., Staab, S.: CREAM: CREAting Metadata for the Semantic Web. Computer Networks: The International Journal of Computer and Telecommunications Networking 42(5), 579–598 (2003)MATHGoogle Scholar
  6. 6.
    Maedche, A., Staab, S.: Ontology Learning for the Semantic Web. IEEE Intelligent Systems 16(2), 72–79 (2001); Special Issue on Semantic WebCrossRefGoogle Scholar
  7. 7.
    Reinberger, M.-L., Spyns, P.: Discovering Knowledge in Texts for the Learning of DOGMA-inspired Ontologies. In: The Proc. ECAI-2004 Workshop on Ontology Learning and Population: Towards Evaluation of Text-based Methods in the Semantic Web and Knowledge Discovery Life Cycle, August 2004, pp. 19–24 (2004)Google Scholar
  8. 8.
    Faure, D., Poibeau, T.: First Experiments of Using Semantic Knowledge Learned by ASIUM for Information Extraction Task Using INTEX. In: The Proc. of the Workshop on Ontology Learning, ECAI 2000, pp. 7–12 (2000)Google Scholar
  9. 9.
    Lee, C.H., Seu, J.H., Evens, M.: Building an Ontology for CIRCSIM-Tutor. In: Proc. 13th Midwest AI and Cognitive Science Society Conference, MAICS 2002, Chicago, pp. 161–168 (2002)Google Scholar
  10. 10.
    Navigli, R., Velardi, P.: Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites. Journal of Computational Linguistics 30(2), 151–179 (June 2004)CrossRefGoogle Scholar
  11. 11.
    Sowa, J.: Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley, Reading (1984)MATHGoogle Scholar
  12. 12.
    Agirre, E., Ansa1, O., Hovy, E.: Enriching very large ontologies using the WWW. In: The Proc. of the Workshop on Ontology Learning, ECAI 2000, pp. 37–42 (2000)Google Scholar
  13. 13.
    Cimiano, P., Pivk, A., Schmidt-Thieme, L., Staab, S.: Learning Taxonomic Relations from Heterogeneous Evidence. In: The Proc. ECAI-2004 Workshop on Ontology Learning and Population (2004)Google Scholar
  14. 14.
    Hearst, M.A.: Automatic Acquisition of Hyponyms from Large Text Corpora. In: Proc. COLING 1992, pp. 539–545 (1992)Google Scholar
  15. 15.
    Cimiano, P., Hotho, A., Staab, S.: Comparing Conceptual, Divisive and Agglomerative Clustering for Learning Taxonomies from Text. In: The Proc. ECAI 2004, pp. 435–439. IOS Press, Amsterdam (2004)Google Scholar
  16. 16.
    Ganter, B., Wille, R.: Formal Concept Analysis – Mathematical Foundations. Springer, Heidelberg (1999)MATHGoogle Scholar
  17. 17.
    Cimiano, P., Staab, S., Tane, J.: Automatic Acquisition of Taxonomies from Text: FCA meets NLP. In: The Proc. of the ECML/PKDD Workshop on Adaptive Text Extraction and Mining, Cavtat–Dubrovnik, Croatia, pp. 10–17 (2003)Google Scholar
  18. 18.
    Hirst, G.: Ontology and the lexicon. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies, pp. 209–229. Springer, Berlin (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Galia Angelova
    • 1
  1. 1.Institute for Parallel ProcessingBulgarian Academy of SciencesSofiaBulgaria

Personalised recommendations