A Hybrid Approach to Ontology Relationship Learning

  • Jon Atle Gulla
  • Terje Brasethvik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5039)


Most ontology learning tools concentrate on extracting concepts and instances from text corpora. There are some recent tools that employ linguistics or data mining to uncover concept relationships, but the results are mixed. Since relationships are semantically complex notions, it seems interesting to combine approaches that address different aspects of concept relationships. In this paper we present a hybrid approach that combines the co-occurrence principle from association rules with contextual similarities from linguistics. The technique has been tested in an ontology engineering project, and the results show significant improvements over traditional techniques.


Association Rule Hybrid Approach Noun Phrase Cosine Similarity Ontology Learning 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining Association Rules between Sets of Items in large Databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data (1993)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of the 20th International Conference on Very Large Data Bases (VLDS 1994) (1994)Google Scholar
  3. 3.
    Cimiano, P., Völker, J.: Text2Onto. In: Montoyo, A., Muńoz, R., Métais, E. (eds.) NLDB 2005. LNCS, vol. 3513, pp. 227–238. Springer, Heidelberg (2005)Google Scholar
  4. 4.
    Cimiano, P., Völker, J., Studer, R.: Ontologies on Demand? A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text. Information, Wissenschaft und Praxis 57(6-7), 315–320 (2006)Google Scholar
  5. 5.
    Cristiani, M., Cuel, R.: A Survey on Ontology Creation Methodologies. Idea Group Publishing (2005)Google Scholar
  6. 6.
    Delgado, M., et al.: Association Rule Extraction for Text Mining. In: Andreasen, T., Motro, A., Christiansen, H., Larsen, H.L. (eds.) FQAS 2002. LNCS (LNAI), vol. 2522. Springer, Heidelberg (2002)Google Scholar
  7. 7.
    Fernandez, M., Goméz-Peréz, A., Juristo, N.: Methontology: from ontological art towards ontological engineering. In: Proceedings of the AAAI 1997 Spring Symposium Series on Ontological Engineering, pp. 33–40. Stanford, Menlo Park (1997)Google Scholar
  8. 8.
    Gaizauskas, R., et al.: GATE User Guide (1996),
  9. 9.
    Gulla, J.A., Borch, H.O., Ingvaldsen, J.E.: Ontology Learning for Search Applications. In: Proceedings of the 6th International Conference on Ontologies, Databases and Applications of Semantics (ODBASE 2007). Springer, Vilamoura (2007)Google Scholar
  10. 10.
    Haddad, H., Chevallet, J., Bruandet, M.: Relations between Terms Discovered by Association Rules. In: Zighed, A.D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910. Springer, Heidelberg (2000)Google Scholar
  11. 11.
    Haase, P., Völker, J.: Ontology Learning and Reasoning - Dealing with Uncertainty and Inconsistency. In: da Costa, P.C.G., et al. (eds.) Proceedings of the International Semantic Web Conference. Workshop 3: Uncertainty Reasoning for the Semantic Web (ISWC-URSW 2005), pp. 45–55. Galway (2005)Google Scholar
  12. 12.
    Ingvaldsen, J.E., et al.: Financial News Mining: Monitoring Continuous Streams of Text. In: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, Hong Kong, December 2006, pp. 321–324 (2006)Google Scholar
  13. 13.
    Maedche, A., Staab, S.: Semi-automatic Engineering of Ontologies from Text. In: Proceedings of the 12th Internal Conference on Software and Knowledge Engineering, Chicago (2000)Google Scholar
  14. 14.
    Navigli, R., Velardi, P.: Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites. Computational Linguistics 30(2), 151–179 (2004)CrossRefGoogle Scholar
  15. 15.
    Nørvåg, K., Eriksen, T.Ø., Skogstad, K.-I.: Mining Association Rules in Temporal Document Collections. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds.) ISMIS 2006. LNCS (LNAI), vol. 4203, pp. 745–754. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Sabou, M., et al.: Learning Domain Ontologies for Semantic Web Service Descriptions. Journal of Web Semantics (accepted, 2008)Google Scholar
  17. 17.
    Solskinnsbakk, G.: Ontology-Driven Query Reformulation in Semantic Search, in Department of Computer and Information Sciences. Norwegian University of Science and Technology, Trondheim (2007)Google Scholar
  18. 18.
    Xu, X., Gulla, J.A.: An information retrieval approach to ontology mapping. Data & Knowledge Engineering 58(1), 47–69 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jon Atle Gulla
    • 1
  • Terje Brasethvik
    • 1
  1. 1.Department of Computer and Information SciencesNorwegian University of Science and TechnologyTrondheim 

Personalised recommendations