Towards Ontology Enrichment with Treatment Relations Extracted from Medical Abstracts

  • Chew-Hung Lee
  • Jin-Cheon Na
  • Christopher Khoo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4312)


In this paper, we present the results of experiments identifying the drug treatment relation and drug treatment attributes like dosage, treatment frequency and duration from abstracts of medical publications using linguistic patterns. The approach uses an automatic linguistic pattern construction algorithm after the dataset has been semantically annotated. The automatically constructed patterns were able to identify treatment relations and their attributes with varying success. We observe that the simple (or naïve) treatment patterns performs much better than the non-naïve treatment patterns in identifying sentences with drug treatment relationship in both cancer and non-cancer drug therapy domain. However the drug dosage, frequency and duration patterns performed much better in the identification of relationships in the cancer drug therapy domain than the non-cancer drug therapy domain.


Digital Library Regular Expression Treatment Pattern Treatment Attribute Semantic Class 
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.
    Swanson, D.R.: Migraine and magnesium: Eleven neglected connections. Perspectives in Biology and Medicine 31(4), 526–557 (1988)Google Scholar
  2. 2.
    Hartel, F.W., de Coronado, S., Dionne, R., Fragoso, G., Golbeck, J.: Modeling a Description Logic Vocabulary for Cancer Research. Journal of Biomedical Informatics 38(2) (2005)Google Scholar
  3. 3.
    Golbeck, J., Fragoso, G., Hartel, F., Hendler, J., Parsia, B., Oberthaler, J.: The National Cancer Institute’s Thesaurus and Ontology. Journal of Web Semantics 1(1), 75–80 (2003)Google Scholar
  4. 4.
    Hahn, U., Schulz, S.: Building a very large ontology from medical thesauri. In: Handbook on Ontologies, pp. 133–150. Springer, Heidelberg (2004)Google Scholar
  5. 5.
    Kavalec, M., Svátek, V.: Relation labelling in ontology learning: Experiments with semantically tagged corpus. In: Proceedings of the EKAW 2004 Workshop on the Application of Language and Semantic Technologies to support Knowledge Management Processes. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Lee, C.H., Na, J.C., Khoo, C.: Ontology learning for medical digital libraries. In: Sembok, T.M.T., Zaman, H.B., Chen, H., Urs, S.R., Myaeng, S.-H. (eds.) ICADL 2003. LNCS, vol. 2911, pp. 302–305. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Soderland, S.: Learning information extraction rules for semi-structured and free text. Machine Learning 34(1-3), 233–272 (1999)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chew-Hung Lee
    • 1
  • Jin-Cheon Na
    • 2
  • Christopher Khoo
    • 2
  1. 1.DSO National LaboratoriesSingaporeSingapore
  2. 2.Division of Information Studies, School of Communication & InformationNanyang Technological UniversitySingaporeSingapore

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