Handbook on Ontologies pp 173-190

Part of the International Handbooks on Information Systems book series (INFOSYS)

Ontology Learning

  • Alexander Maedche
  • Steffen Staab

Summary

Ontology Learning greatly facilitates the construction of ontologies by the ontology engineer. The notion of ontology learning that we propose here includes a number of complementary disciplines that feed on different types of unstructured and semi-structured data in order to support a semi-automatic, cooperative ontology engineering process. Our ontology learning framework proceeds through ontology import, extraction, pruning, and refinement, giving the ontology engineer a wealth of coordinated tools for ontology modelling. Besides of the general architecture, we show in this paper some exemplary techniques in the ontology learning cycle that we have implemented in our ontology learning environment, KAON Text-To-Onto.

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References

  1. 1.
    Agrawal, R. and Imielinski, T. and Swami, A.: Mining Associations between Sets of Items in Massive Databases, In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., May 26–28, 1993.Google Scholar
  2. 2.
    H. Assadi. Construction of a regional ontology from text and its use within a documentary system. In Proceedings of the International Conference on Formal Ontology and Information Systems - FOIS’98, Trento, Italy, 1998.Google Scholar
  3. 3.
    N. Aussenac-Gilles, and A. Maedche (eds.). Workshop on Machine Learning and Natural Language Processing for Ontology Engineering, http://www.sop.inria.fr/acacia/OLT2002Google Scholar
  4. 4.
    R. Basili, M. T. Pazienza, and P. Velardi. Acquisition of selectional patterns in a sublanguage. Machine Translation, 8 (1): 175–201, 1993.CrossRefGoogle Scholar
  5. 5.
    Paul Buitelaar. CORELEX: Systematic Polysemy and Underspecification. PhD thesis, Brandeis University, Department of Computer Science, 1998.Google Scholar
  6. 6.
    P. Chapman, R. Kerber, J. Clinton, T. Khabaza, T. Reinartz, and R. Wirth. The CRISP-DM Process Model. Discussion Paper, March 1999. http://www.crisp-dm.org/ Google Scholar
  7. 7.
    H. Cunningham and R. Gaizauskas and K. Humphreys and Y. Wilks: Three Years of GATE, In Proceedings of the AISB’99 Workshop on Reference Architectures and Data Standards for NLP, Edinburgh, U.K. Apr, 1999.Google Scholar
  8. 8.
    F. Esposito, S. Ferilli, N. Fanizzi, and G. Semeraro. Learning from parsed sentences with inthelex. In Proceedings of Learning Language in Logic Workshop (LLL-2000), Lisbon, Portugal, 2000, 2000.Google Scholar
  9. 9.
    D. Faure and C. Nedellec. A corpus-based conceptual clustering method for verb frames and ontology acquisition. In LREC workshop on adapting lexical and corpus resources to sublanguages and applications, Granada, Spain, 1998.Google Scholar
  10. 10.
    B. Ganter and R. Wille. Formal Concept Analysis: Mathematical Foundations. Springer, Berlin - Heidelberg - New York, 1999.CrossRefGoogle Scholar
  11. 11.
    A. Gomez-Perez. Ontology Engineering. Springer Verlag, 2002/2003.Google Scholar
  12. 12.
    U. Hahn and K. Marko: An integrated, dual learner for grammars and ontologies. Data and Knowledge Engineering, 42 (3): 273–291, 2002.CrossRefGoogle Scholar
  13. 13.
    U. Hahn and M. Romacker. Content management in the syndikate system–how technical documents are automatically transformed to text knowledge bases. Data & Knowledge Engineering, 35: 137–159, 2000.CrossRefGoogle Scholar
  14. 14.
    U. Hahn and K. Schnattinger. Towards text knowledge engineering. In Proc. ofAAAI ‘88, pages 129–144, 1998.Google Scholar
  15. 15.
    L. Kaufman and P. Rousseeuw: Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley, 1990.CrossRefGoogle Scholar
  16. 16.
    Hearst, M.: Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th International Conference on Computational Linguistics. Nantes, France, 1992.Google Scholar
  17. 17.
    J. Jannink and G. Wiederhold. Thesaurus entry extraction from an on-line dictionary. In Proceedings of Fusion ‘89, Sunnyvale CA, July 1999, 1999. http://wwwdb.stanford.edu/SKC/publications.html.Google Scholar
  18. 18.
    P. Johannesson. A method for transforming relational schemas into conceptual schemas. In M. Rusinkiewicz, editor, 10th International Conference on Data Engineering, pages 115–122, Houston, 1994. IEEE Press.Google Scholar
  19. 19.
    Kietz, J.-U. and Volz, R. and Maedche, A.: Semi-automatic ontology acquisition from a corporate intranet. In International Conference on Grammar Inference (ICGI-2000), Lecture Notes in Artificial Intelligence, LNAI, 2000.Google Scholar
  20. 20.
    J.-U. Kietz and K. Morik. A polynomial approach to the constructive induction of structural knowledge. Machine Learning, 14 (2): 193–218, 1994.CrossRefGoogle Scholar
  21. 21.
    L. Lee. Measures of distributional similarity. In Proc. of the 37th Annual Meeting of the Association for Computational Linguistics, 1999, pp. 25–32.Google Scholar
  22. 22.
    A. Maedche: Ontology Learning for the Semantic Web. Kluwer Academic Publishers, 2002.Google Scholar
  23. 23.
    A. Maedche, B. Motik, L. Stojanovic, R. Studer, and R. Volz. An infrastructure for searching, reusing and evolving distributed ontologies. In Proceedings 12th International World Wide Web Conference (WWW12), Semantic Web Track, 2003, Budapest, Hungary, pp. 439–448.Google Scholar
  24. 24.
    A. Maedche, V. Pekar and S. Staab. Ontology Learning Part One–On Discoverying Taxonomic Relations from the Web. In: Ning Zhong et al. (eds) Web Intelligence, Springer, 2003, pp. 301–320.Google Scholar
  25. 25.
    A. Maedche and S. Staab. Discovering conceptual relations from text. In Proceedings of ECAI-2000. I05 Press, Amsterdam, 2000.Google Scholar
  26. 26.
    A. Maedche and S. Staab. Mining ontologies from text. In Proceedings of EKAW-2000, Springer Lecture Notes in Artificial Intelligence (LNAI-1937), Juan-Les-Pins, France, 2000.Google Scholar
  27. 27.
    A. Maedche and S. Staab. Measuring Similarity between Ontologies. In: Proc. Of the European Conference on Knowledge Acquisition and Management–EKAW-2002. Madrid, Spain, October 1–4, 2002. LNCS/LNAI 2473, Springer, 2002, pp. 251–263.Google Scholar
  28. 28.
    A. Maedche, S. Staab, E. Hovy, and C. Nedellec (eds.). The IJCAI-2001 Workshop on Ontology Learning. Proceedings of the Second Workshop on Ontology Learning - OL’2001,Seattle, WA, USA, August 4, 2001. CEUR Proceedings.Google Scholar
  29. 29.
    A. Maedche, B. Motik, L. Stojanovic, R. Studer, and R. Volz: Ontologies for Enterprise Knowledge Management, IEEE Intelligent Systems, December, 2002.Google Scholar
  30. 30.
    Manning, C. and Schuetze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge, Massachusetts, 1999.Google Scholar
  31. 31.
    A. Mikheev and S. Finch. A workbench for finding structure in text. In In Proceedings of the 5th Conference on Applied Natural Language Processing — ANLP’97, March 1997, Washington DC, USA, pages 372–379, 1997.Google Scholar
  32. 32.
    G. Miller. WordNet: A Lexical Database for English. Communications of the ACM,38(11), pp. 3941.Google Scholar
  33. 33.
    K. Morik and S. Wrobel and J.-U. Kietz and W. Emde Knowledge acquisition and machine learning: Theory, methods, and applications, London: Academic Press, 1993.Google Scholar
  34. 34.
    E. Morin. Automatic acquisition of semantic relations between terms from technical corpora. In Proc. of the Fifth International Congress on Terminology and Knowledge Engineering - TKE’99, 1999.Google Scholar
  35. 35.
    G. Neumann and R. Backofen and J. Baur and M. Becker and C. Braun: An Information Extraction Core System for Real World German Text Processing. In Proceedings of ANLP-97, Washington, USA, 1997.Google Scholar
  36. 36.
    M. Missikoff, R. Navigli, and P. Velardi: The Usable Ontology: An Environment for Building and Assessing a Domain Ontology. Proceedings of the International Semantic Web Conference 2002. Springer, 2002, pp. 39–53.Google Scholar
  37. 37.
    B. Motik and A. Maedche and R. Volz: A Conceptual Modeling Approach for building semantics-driven enterprise applications. 1st International Conference on Ontologies, Databases and Application of Semantics (ODBASE-2002), California, USA, 2002.Google Scholar
  38. 38.
    H. A. Mueller, J. H. Jahnke, D. B. Smith, M.-A. Storey, S. R. Tilley, and K. Wong. Reverse Engineering: A Roadmap. In Proceedings of the 22nd International Conference on Software Engineering (ICSE-2000), Limerick, Ireland. Springer, 2000.Google Scholar
  39. 39.
    D. Oberle, R. Volz, S. Staab, and B. Motik. An extensible ontology software environment. In this book.Google Scholar
  40. 40.
    V. Pekar and S. Staab. Taxonomy Learning — Factoring the structure of a taxonomy into a semantic classification decision. In: Proceedings of the 19th Conference on Computational Linguistics, COLING-2002, August 24–September 1, 2002, Taipei, Taiwan, 2002.Google Scholar
  41. 41.
    Pereira, F. and Tishby, N. and Lee, L.: Distributation Clustering of English Words. In Proceedings of the ACL-93, 1993.Google Scholar
  42. 42.
    Porter, M. F.: An algorithm for suffix stripping. In Program,14(3), 1980, pp. 130137.Google Scholar
  43. 43.
    P. Resnik. Selection and Information: A Class-based Approach to Lexical Relationships. PhD thesis, University of Pennsylania, 1993.Google Scholar
  44. 44.
    S. Schlobach. Assertional mining in description logics. In Proceedings of the 2000 International Workshop on Description Logics (DL2000),2000. http: //SunSITE.Informatik.RWTH-Aachen.DE/Publications/CEUR-W SNol-33/.Google Scholar
  45. 45.
    S. Staab, A. Maedche, C. Nedellec, and P. Wiemer-Hastings (eds.). The ECAI’2000 Workshop on Ontology Learning. Proceedings of the First Workshop on Ontology Learning - OL’2000,Berlin, Germany, August 25, 2000. CEUR Proceedings, Vol-31.Google Scholar
  46. 46.
    L. Stojanovic, N. Stojanovic, and R. Volz. A reverse engineering approach for migrating data-intensive web sites to the semantic web. In HP-2002, August 25–30, 2002, Montreal, Canada (Part of the IFIP World Computer Congress WCC2002), 2002.Google Scholar
  47. 47.
    G. Stumme, R. Taouil, Y. Bastide, N. Pasqier, and L. Lakhal. Computing Iceberg Concept Lattices with Titanic. Journal on Knowledge and Data Engineering,42(2), pp. 189222.Google Scholar
  48. 48.
    Y. Sure, J. Angele, and S. Staab. OntoEdit: Guiding Ontology Development by Methodology and Inferencing In S. Spaccapietra, S. March, and K. Aberer (eds.). LNCS - Semantics of Data, Springer, 2003 (to appear). (Extended version from ODBase-2002).Google Scholar
  49. 49.
    Y. Sure, S. Staab, and R. Studer. Methodology for Development and Employment of Ontology based Knowledge Management Applications In this book.Google Scholar
  50. 50.
    Z. Tari, O. Bukhres, J. Stokes, and S. Hammoudi. The Reengineering of Relational Databases based on Key and Data Correlations. In Proceedings of the 7th Conference on Database Semantics (DS-7), 7–10 October 1997, Leysin, Switzerland. Chapman & Hall, 1998.Google Scholar
  51. 51.
    P. Wiemer-Hastings, A. Graesser, and K. Wiemer-Hastings. Inferring the meaning of verbs from context. In Proceedings of the Twentieth Annual Conference of the Cognitive Science Society, 1998.Google Scholar
  52. 52.
    Y. Wilks, B. Slator, and L. Guthrie. Electric Words: Dictionaries, Computers, and Meanings. MIT Press, Cambridge, MA, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Alexander Maedche
    • 1
  • Steffen Staab
    • 2
  1. 1.FZI Research Center for Information TechnologiesUniversity of KarlsruheGermany
  2. 2.Institute AIFBUniversity of KarlsruheGermany

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