A new Formal Concept Analysis based learning approach to Ontology building

Formal Concept Analysis (FCA) is a concept clustering approach that has been widely applied in ontology learning. In our work, we present an innovative approach to generating information context from a tentative domain specified scientific corpus and mapping a concept lattice to a formal ontology. The application of the proposed approach to Semantic Web search demonstrates this automatically constructed ontology can provide a semantic way to expand users' query context, which can complement a conventional search engine.


Information Context Concept Lattice Query Term Query Expansion Formal Context 
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.
    [1] Guarino, N., Giaretta, P.: Ontologies and Knowledge Bases: Towards a Terminological Clarification. IOS Press, Amsterdam (1995)Google Scholar
  2. 2.
    [2] Krovetz, R., Croft, W.B.: Lexical Ambiguity and Information Retrieval. Lexical Acquisition: exploiting on-line resources to build a lexicon, pp.45–65. Hillsdale, New Jersey, Lawrence Erlbaum Associates (1991)Google Scholar
  3. 3.
    Novacek, V., Smrz, P., Pomikalek, J.: Text Mining for Semantic Relations as Support Base of a Scientific Portal Generation. In Proceedings of 5th International Conference on Language Resources and Evaluation, pp1338–1343. ELRA, Genova (2006)Google Scholar
  4. 4.
    [4]Quan, T., Hui, S., Fong., A., Cao,T.: Automatic Generation of Ontology for Scholarly Semantic Web. In The Semantic Web — ISWC 2004, LNCS, pp726–pp740. Springer, Hiroshima (2004)Google Scholar
  5. 5.
    Zhang, G., Troy,A., and Bourgoin, K.: Bootstrapping Ontology Learning for Information Retrieval Using Formal Concept Analysis and Information Anchors. In 14th International Conference on Conceptual Structures. Alborg (2006)Google Scholar
  6. 6.
    [6]Zhao, P., Zhang, M., D., Tang, S.: Finding Hidden Semantics behind Reference Linkages: an Ontological Approach for Scientific Digital Libraries. In The Database Systems for Advanced Applications, 10th International Conference, LNCS, pp699-710. Springer, Beijing (2005)Google Scholar
  7. 7.
    Makagonov, p., Figueroa, A., Sboychakov, K., Gelbukh, A.: Learning a Domain Ontology from Hierarchically Structured Texts. In Proc. of Workshop “Learning and Extending Lexical Ontologies by using Machine Learning Methods”. At 22nd International Conference on Machine learning. Bonn (2005)Google Scholar
  8. 8.
    [8]Hearst, M., A.: Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th conference on Computational linguistics, pp539–545. Morrisotown, NJ, USA (1992)CrossRefGoogle Scholar
  9. 9.
    Cimiano,P., Pivk,A., Thieme,L.: Learning Taxonomic Relations from Heterogeneous Sources of Evidence. Ontology Learning from Text: Methods, Evaluation and Applications Volume 123 of Frontiers in Artificial Intelligence, pp 59–73. ISO Press (2005)Google Scholar
  10. 10.
    Bisson, G., Nédellec, C., Caň amero, L.: Designing clustering methods for ontology building — The Mo'k workbench' in proceedings of the ECAI Ontology Learning Workshop. Berlin (2000)Google Scholar
  11. 11.
    Cimiano, P., Hotho, A., Staab, S.: Comparing conceptual, divisive and agglomerative clustering for learning taxonomies from text. In Proceedings of the European Conference on Artificial Intelligence (ECAI), pp 435–439. Valencia (2004)Google Scholar
  12. 12.
    [12]Carpineto, C., Romano, G.: Concept Data Analysis — Theory and Applications. John Wiley & Sons Ltd, England. (2004)MATHGoogle Scholar
  13. 13.
    [13] ToscanaJ Suite, http://toscanaj.sourceforge.net.

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.School of Engineering and ComputingGlasgow Caledonian UniversityGlasgowUK

Personalised recommendations