Building Medical Ontologies Based on Terminology Extraction from Texts: Methodological Propositions

  • Audrey Baneyx
  • Jean Charlet
  • Marie-Christine Jaulent
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3581)


In the medical field, it is now established that the maintenance of unambiguous thesauri is accomplished by the building of ontologies. Our task in the PertoMed project is to help pneumologists code acts and diagnoses with a software that represents medical knowledge by an ontology of the concerned specialty. We apply natural language processing tools to corpora to develop the resources needed to build this ontology. In this paper, our objective is to develop a methodology for the knowledge engineer to build various types of medical ontologies based on terminology extraction from texts according to the differential semantics theory. Our main research hypothesis concerns the joint use of two methods: distributional analysis and recognition of semantic relationships by lexico-syntactic patterns. The expected result is the building of an ontology of pneumology.


Noun Phrase Semantic Relationship Knowledge Engineer Candidate Term Lexical Unit 
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.
    Rector, A.: Thesauri and formal classifications: Terminologies for people and machines. Methods of Information in Medicine 37, 501–509 (1998)Google Scholar
  2. 2.
    Staab, S., Studer, R.: Handbook on Ontologies, 1st edn. Springer, Heidelberg (2003)Google Scholar
  3. 3.
    Charlet, J., Bachimont, B., Jaulent, M.: Building medical ontologies by terminology extraction from texts: An experiment for the intensive care units. Computer in Biology and Medicine (2005) (to appear)Google Scholar
  4. 4.
    Bachimont, B., Isaac, A., Troncy, R.: Semantic commitment for designing ontologies: A proposal. In: Gómez-Pérez, A., Benjamins, V.R. (eds.) EKAW 2002. LNCS (LNAI), vol. 2473, pp. 114–121. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Harris, Z.: Mathematical Structures of Language. John Wiley and Sons, NewYork (1968)zbMATHGoogle Scholar
  6. 6.
    Le Moigno, S., Charlet, J., Bourigault, D., Degoulet, P., Jaulent, M.: Terminology extraction from text to build an ontology in surgical intensive care. In: Proceedings of the AMIA Annual Symposium 2002, San Antonio, Texas, pp. 430–435 (2002)Google Scholar
  7. 7.
    Malaisé, V., Zweigenbaum, P., Bachimont, B.: Mining defining contexts to help structuring differential ontologies. In: Ibekwe-San, J., Condamines, A., Cabré, T. (eds.) Application-Driven Terminology Engineering, Termonology, pp. 21–53. John Benjamins, Amsterdam (2005)Google Scholar
  8. 8.
    Hearst, M.: Automatic acquisition of hyponyms from large text corpora. In: Zampolli, A. (ed.) Proceedings of the 14th COLING, Nantes, France, pp. 539–545 (1992)Google Scholar
  9. 9.
    Trombert-Paviot, B., Rodrigues, J., Rogers, J., Baud, R., van der Haring, E., Rassinoux, A., Abrial, V., Clavel, L., Idir, H.: Galen: a third generation terminology tool to support a multipurpose national coding system for surgical procedures. International Journal of Medical Informatics 58-59, 71–85 (2000)CrossRefGoogle Scholar
  10. 10.
    Zweigenbaum, P., Bouaud, J., Bachimont, B., Charlet, J., Séroussi, B., Boisvieux, J.F.: From text to knowledge: a unifying document-oriented view of analyzed medical language. Methods of Information in Medicine 37, 384–393 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Audrey Baneyx
    • 1
  • Jean Charlet
    • 1
    • 2
  • Marie-Christine Jaulent
    • 1
  1. 1.INSERM, U729ParisFrance
  2. 2.STIM – DSI/AP-HP 

Personalised recommendations