Formal Concept Analysis: A Unified Framework for Building and Refining Ontologies

  • Rokia Bendaoud
  • Amedeo Napoli
  • Yannick Toussaint
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5268)


Building a domain ontology usually requires several resources of different types, e.g. thesaurus, object taxonomies, terminologies, databases, sets of documents, etc, where objects are described in terms of attributes and relations with other objects. One important and hard problem is to be able to combine and merge knowledge units extracted from these different resources within an homogeneous formal representation (such as a description logic or OWL). The purpose of this article is to show which kinds of resources should be available for designing a real-world ontology in a given application domain, and then how Formal Concept Analysis and its extension - Relational Concept Analysis- can be used for materializing an associated ontology. This resulting target ontology can then be encoded within OWL or a description logic formalism, allowing classification-based reasoning. A real-world example in microbiology is detailed. Finally, an evaluation including tests on recall and precision shows how source resources can be completed with other existing domain resources using a semi-automatic analysis process.


Description Logic Neisseria Gonorrhoeae Concept Lattice Domain Ontology 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.


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  1. 1.
    Maedche, A.: Ontologies Learning for the Semantic Web. Springer, Heidelberg (2002)Google Scholar
  2. 2.
    Maedche, A., Staab, S.: Discovering conceptual relation from text. In: 14th European Conference on Artifical Intelligence (ECAI 2000), Berlin, Germany, pp. 321–325 (2000)Google Scholar
  3. 3.
    Ganter, B., Wille, R.: Formal Concept Analysis, Mathematical Foundations. Springer, Heidelberg (1999)zbMATHGoogle Scholar
  4. 4.
    Faure, D., Nedellec, C.: A corpus-based conceptual clustering method for verb frames and ontology acquisition. In: Workshop on Adapting lexical and corpus resources to sublanguages and applications (LREC 1998) (1998)Google Scholar
  5. 5.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The description logic handbook: theory, implementation, and applications. Cambridge University Press, New York (2003)zbMATHGoogle Scholar
  6. 6.
    Stumme, G., Maedche, A.: Fca-merge: Bottom-up merging of ontologies. In: International Joint Conference on Artificial Intelligence (IJCAI 2001), pp. 225–234 (2001)Google Scholar
  7. 7.
    Cooper, J.W., Byrd, R.J.: Lexical navigation: Visually prompted query expansion and refinement. In: 2nd International Conference on Digital Libraries (DL 1997), pp. 237–246 (1997)Google Scholar
  8. 8.
    Pan, J.Z., Serafini, L., Zhao, Y.: Semantic import: An approach for partial ontology reuse. In: 1st International Workshop on Modular Ontologies (WoMO 2006) In ISWC 2006 (2006)Google Scholar
  9. 9.
    Ding, L., Finin, T.W., Joshi, A., Pan, R., Scott Cost, R., Peng, Y., Reddivari, P., Doshi, V., Sachs, J.: Swoogle: a search and metadata engine for the semantic web. In: Grossman, D., Gravano, L., Zhai, C., Herzog, O., Evans, D.A. (eds.) International Conference on Information and Knowledge Management (CIKM 2004), pp. 652–659. ACM, New York (2004)Google Scholar
  10. 10.
    Rouane-Hacene, M., Huchard, M., Napoli, A., Valtchev, P.: A proposal for combining formal concept analysis and description logics for mining relational data. In: Kuznetsov, S.O., Schmidt, S. (eds.) ICFCA 2007. LNCS (LNAI), vol. 4390, pp. 51–65. Springer, Heidelberg (2007)Google Scholar
  11. 11.
    Aussenac-Gilles, N., Biébow, B., Szulman, S.: Revisiting ontology design: A method based on corpus analysis. In: Dieng, R., Corby, O. (eds.) EKAW 2000. LNCS (LNAI), vol. 1937, pp. 172–188. Springer, Heidelberg (2000)Google Scholar
  12. 12.
    Cimiano, P., Hotho, A., Staab, S.: Learning concept hierarchies from text corpora using formal concept analysis. Journal of Artificial Intelligence Research (JAIR) 24, 305–339 (2005)zbMATHGoogle Scholar
  13. 13.
    Bendaoud, R., Rouane-Hacene, M., Toussaint, Y., Delecroix, B., Napoli, A.: Text-based ontology construction using relational concept analysis. In: Flouris, G., d’Aquin, M. (eds.) Proceedings of the International Workshop on Ontology Dynamics, Innsbruck, Austria, pp. 55–68 (2007)Google Scholar
  14. 14.
    Navigli, R., Velardi, P.: Ontology enrichment through automatic semantic annotation of on-line glossaries. In: Staab, S., Svátek, V. (eds.) EKAW 2006. LNCS (LNAI), vol. 4248, pp. 126–140. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Gruber, T.R.: Toward principales for the design of ontologies used for knowledge sharing. In: Guarino, N., Poli, R. (eds.) Formal Analysis in Conceptual Analysis and Knowledge Representation, The Netherlands. Kluwer Academic, Dordrecht (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rokia Bendaoud
    • 1
  • Amedeo Napoli
    • 1
  • Yannick Toussaint
    • 1
  1. 1.UMR 7503 LORIANancyFrance

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