Domain Model for Medical Information Extraction—The LightMedOnt Ontology

  • Agnieszka Mykowiecka
  • Małgorzata Marciniak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5070)


The paper describes the creation of a domain model for an Information Extraction (IE) application in the medical domain. First, we present texts: mammography reports and diabetology patients’ discharge documents, for which IE systems were created. The methodology and results of terminology extraction for both domains are described. Next, the main features and the upper part of LightMedOnt—medical ontology in OWL formalism are presented. In the final part of the paper we discuss the relationships between OWL ontologies and the domain model of the IE system used for our experiments.


domain model ontology terminology extraction clinical data processing 


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  1. 1.
    Becker, M., Drożdżyński, W., Krieger, H., Piskorski, J., Schaefer, U., Becker, F.X.: SProUT — Shallow Processing with Typed Feature Structures and Unification. In: Proceedings of ICON 2002, Mumbai, India (2002)Google Scholar
  2. 2.
    Berners-Lee, T., Hendler, J., Lasila, O.: The Semantic Web. Scientific American (May 2001)Google Scholar
  3. 3.
    Bontas, E.P., Mochol, M., Tolksdorf, R.: Case studies on ontology reuse. In: Proc. of 5th International Conference on Knowledge Management (I-Know’05), Graz, Austria (2005)Google Scholar
  4. 4.
    Dasmahapatra, S., Dupplaw, D., Bo, H., Lewis, H., Lewis, P., Shadbolt, N.: Facilitating multi-disciplinary knowledge-based support for breast cancer screening. Int. J. of Healthcare Technology and Management 7, 403–420 (2006)CrossRefGoogle Scholar
  5. 5.
    Drożdżyński, W., Krieger, H.-U., Piskorski, J., Schäfer, U., Xu, F.: Shallow Processing with Unification and Typed Feature Structures — Foundations and Applications. German AI Journal KI-Zeitschrift 01/04 (2004)Google Scholar
  6. 6.
    Emele, M.C.: The typed feature structure representation formalism. In: Proceedings of the International Workshop on Sharable Natural Language Resources, Ikoma, Nara, Japan (1994)Google Scholar
  7. 7.
    Frantzi, K., Ananiado, S., Mima, H.: Automatic recognition of multi-word terms: the C-value /NC-value method. International Journal on Digital Libraries, 15–130 (2000)Google Scholar
  8. 8.
    Frantzi, K., Ananiadou, S., Mima, H.: Automatic recognition of multi-word terms: the C-value/NC-value method. International Journal of Digital Libraries (2000)Google Scholar
  9. 9.
    Gruber, T.R.: A translation approach to portable ontology specifications. Knowledge Acquisition 5, 199–220 (1993)CrossRefGoogle Scholar
  10. 10.
    Gruber, T.R.: Ontolgy. In: Ozsu, M.T., Liu, L. (eds.) Encyclopedia of Database Systems, Springer, Heidelberg (2009)Google Scholar
  11. 11.
    Guarino, N., Giaretta, P.: Ontologies and knowledge bases: Towards a terminological clarification. In: Mars, N.J.I. (ed.) Towards Very Large Knowledge Bases, IOS Press, Amsterdam (1995)Google Scholar
  12. 12.
    Guizzardi, G., TerryHalpin: Ontological foundations for conceptual modelling. Applied Ontology, 1–12 (2008)Google Scholar
  13. 13.
    Marciniak, M., Mykowiecka, A., Kupść, A., Piskorski, J.: Intelligent content extraction from polish medical reports. In: Bolc, L., Michalewicz, Z., Nishida, T. (eds.) IMTCI 2004. LNCS (LNAI), vol. 3490, pp. 68–78. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Mykowiecka, A., Kupść, A., Marciniak, M.: Rule-based medical content extraction and classification. In: Intelligent Information Processing and Web Mining Proceedings of the International IIS: IIPWM’05, Springer, Heidelberg (2005)Google Scholar
  15. 15.
    Mykowiecka, A., Marciniak, M.: Domain-driven automatic spelling correction for mammography reports. In: Intelligent Information Processing and Web Mining Proceedings of the International IIS: IIPWM’06. Advances in Soft Computing, Springer, Heidelberg (2006)Google Scholar
  16. 16.
    Mykowiecka, A., Marciniak, M., Kupść, A.: Rule-based information extraction from patient’s clinical data. J Biomed Inform (in press), doi:10.1016/j.jbi.2009.07.007Google Scholar
  17. 17.
    Mykowiecka, A., Marciniak, M., Podsiadły-Marczynkowska, T.: A “data-driven” ontology for an information extraction system from mammography reports. In: Proceedings of 10th Intl. Protégé Conference (2007)Google Scholar
  18. 18.
    Piasecki, M., Godlewski, G.: Reductionistic, tree and rule based tagger for Polish. In: Intelligent Information Processing and Web Mining Proceedings of the International IIS: IIPWM’06, pp. 531–540. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  19. 19.
    Podsiadły-Marczykowska, T., Guzik, A.: Mammographic ontology - conceptual model of the domain. The International Journal of Artificial Organs (2004)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Agnieszka Mykowiecka
    • 1
  • Małgorzata Marciniak
    • 1
  1. 1.Institute of Computer SciencePolish Academy of SciencesWarsawPoland

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