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Text2Onto

A Framework for Ontology Learning and Data-Driven Change Discovery
  • Philipp Cimiano
  • Johanna Völker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3513)

Abstract

In this paper we present Text2Onto, a framework for ontology learning from textual resources. Three main features distinguish Text2Onto from our earlier framework TextToOnto as well as other state-of-the-art ontology learning frameworks. First, by representing the learned knowledge at a meta-level in the form of instantiated modeling primitives within a so called Probabilistic Ontology Model (POM), we remain independent of a concrete target language while being able to translate the instantiated primitives into any (reasonably expressive) knowledge representation formalism. Second, user interaction is a core aspect of Text2Onto and the fact that the system calculates a confidence for each learned object allows to design sophisticated visualizations of the POM. Third, by incorporating strategies for data-driven change discovery, we avoid processing the whole corpus from scratch each time it changes, only selectively updating the POM according to the corpus changes instead. Besides increasing efficiency in this way, it also allows a user to trace the evolution of the ontology with respect to the changes in the underlying corpus.

Keywords

Modeling Primitive Ontology Learning Explanation Component Knowledge Representation Language Mereological Relation 
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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References

  1. 1.
    Alfonseca, E., Manandhar, S.: Extending a lexical ontology by a combination of distributional semantics signatures. In: Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management, EKAW (2002)Google Scholar
  2. 2.
    Bisson, G., Nedellec, C., Canamero, L.: Designing clustering methods for ontology building - The Mo’K workbench. In: Proceedings of the ECAI Ontology Learning Workshop, pp. 13–19 (2000)Google Scholar
  3. 3.
    Buitelaar, P., Olejnik, D., Sintek, M.: OntoLT: A protégé plug-in for ontology extraction from text. In: Proceedings of the International Semantic Web Conference, ISWC (2003)Google Scholar
  4. 4.
    Charniak, E., Berland, M.: Finding parts in very large corpora. In: Proceedings of the 37th Annual Meeting of the ACL, pp. 57–64 (1999)Google Scholar
  5. 5.
    Cimiano, P., Pivk, A., Schmidt-Thieme, L., Staab, S.: Learning taxonomic relations from heterogeneous sources. In: Proceedings of the ECAI 2004 Ontology Learning and Population Workshop (2004)Google Scholar
  6. 6.
    Cimiano, P., Völker, J.: Towards large-scale, unsupervised and ontology-based named entity recognition. Technical Report. AIFB, University of Karlsruhe (2004)Google Scholar
  7. 7.
    Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: GATE: A framework and graphical development environment for robust NLP tools and applications. In: Proceedings of the 40th Annual Meeting of the ACL (2002)Google Scholar
  8. 8.
    Faure, D., Nedellec, C.: A corpus-based conceptual clustering method for verb frames and ontology. In: Proceedings of the LREC Workshop on Adapting lexical and corpus resources to sublanguages and applications (1998)Google Scholar
  9. 9.
    Fellbaum, C.: WordNet, an electronic lexical database. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  10. 10.
    Frantzi, K., Ananiadou, S., Tsuji, J.: The c-value/nc-value method of automatic recognition for multi -word terms. In: Proceedings of the ECDL, pp. 585–604 (1998)Google Scholar
  11. 11.
    Gruber, T.: A translation approach to portable ontology specifications. Knowledge Acquisition 5(2), 199–220 (1993)CrossRefGoogle Scholar
  12. 12.
    Hahn, U., Schnattinger, K.: Towards text knowledge engineering. In: AAAI/IAAI, pp. 524–531 (1998)Google Scholar
  13. 13.
    Hearst, M.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th International Conference on Computational Linguistics, pp. 539–545 (1992)Google Scholar
  14. 14.
    Kifer, M., Lausen, G., Wu, J.: Logical foundations of object-oriented and framebased languages. Journal of the ACM 42, 741–843 (1995)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Lee, L.: Measures of distributional similarity. In: 37th Annual Meeting of the Association for Computational Linguistics, pp. 25–32 (1999)Google Scholar
  16. 16.
    Maedche, A., Staab, S.: Discovering conceptual relations from text. In: Horn, W. (ed.) Proceedings of the 14th European Conference on Artificial Intellignece, ECAI’2000 (2000)Google Scholar
  17. 17.
    Maedche, A., Staab, S.: Ontology learning. In: Staab, S., Studer, R. (eds.) Handbook on Ontologies, pp. 173–189. Springer, Heidelberg (2004)Google Scholar
  18. 18.
    Pinto, H.S., Tempich, C., Staab, S.: Diligent: Towards a fine-grained methodology for distributed, loosely-controlled and evolving engingeering of ontologies. In: Proceedings of the 16th European Conference on Artificial Intelligence, ECAI (2004)Google Scholar
  19. 19.
    Staab, S., Erdmann, E., Maedche, A.: Engineering ontologies using semantic patterns. In: Proceedings of the IJCAI 2001 Workshop on E-Business and Intelligent Web (2001)Google Scholar
  20. 20.
    Stojanovic, L.: Methods and Tools for Ontology Evolution. PhD thesis, University of Karlsruhe (2004) Google Scholar
  21. 21.
    Velardi, P., Navigli, R., Cuchiarelli, A., Neri, F.: Evaluation of ontolearn, a methodology for automatic population of domain ontologies. In: Buitelaar, P., Cimiano, P., Magnini, B. (eds.) Ontology Learning from Text: Methods, Applications and Evaluation, IOS Press, Amsterdam (2005) (to appear)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Philipp Cimiano
    • 1
  • Johanna Völker
    • 1
  1. 1.Institute AIFBUniversity of Karlsruhe 

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