Automatic Supply of a Medical Knowledge Base Using Linguistic Methods

  • Grażyna Szostek
  • Marek Jaszuk
Part of the Studies in Computational Intelligence book series (SCI, volume 369)


The paper presents a methodology for creating a semantic model of disease symptoms. The source material for the methodology are medical texts describing the symptoms. Semantic descriptions are automatically extracted from text using natural language processing methods and lexical resources. The methods transform text describing symptoms into a set of rules constituting their semantic descriptions. The descriptions consist of a number of concepts and relations between them. Combining all such descriptions forms a model of the disease symptoms.


semantic model medical knowledgebase natural language processing 


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  1. 1.
    Braun-Falco, O., Plewig, G., Wolff, H.H., et al.: Dermatologia. Czelej, Lublin (2004)Google Scholar
  2. 2.
    Broda, B., Derwojedowa, M., Piasecki, M.: Recognition of Structured Collocations in An Inflective Language. In: Proceedings of the International Multiconference on Computer Science and Information Technology - 2nd International Symposium Advances in Artificial Intelligence and Applications (AAIA 2007), pp. 237–246 (2007)Google Scholar
  3. 3.
    Broda, B., Derwojedowa, M., Piasecki, M., Szpakowicz, S.: Corpus-based Semantic Relatedness for the Construction of Polish WordNet. In: Proceedings of the 6th Language Resources and Evaluation Conference, LREC 2008 (2008)Google Scholar
  4. 4.
    Ceglarek, D., Rutkowski, W.: Automated Acquisition of Semantic Knowledge to Improve Efficiency of Information Retrieval Systems. In: 9th International Conference on Business Information Systems (BIS 2006). LNI - Proceedings, Klagenfurt, Austria, Bonn, May 31-June 2 (2006)Google Scholar
  5. 5.
    Cimiano, P., Staab, S.: Learning concept hierarchies from text with a guided agglomerative clustering algorithm. In: Proceedings of the Workshop on Learning and Extending Lexical Ontologies with Machine Learning Methods, Bonn, Germany (2005)Google Scholar
  6. 6.
    Derwojedowa, M., Piasecki, M., Szpakowicz, S., Zawisł awska, M., Broda, B.: Words, Concepts and Relations in the Construction of Polish WordNet. In: Tanâcs, A., Csendes, D., Vincze, V., Fellbaum, C., Vossen, P. (eds.) Proceedings of the Global WordNet Conference, vol. 22-25, pp. 162–177. University of Szeged, Hungary (2008)Google Scholar
  7. 7.
    Faure, D., Nedellec, C.: Knowledge acquisition of predicate argument structures from technical texts using machine learning: The system asium. In: Proceedings of the 11th European Workshop on Knowledge Acquisition, Modeling and Management (EKAW), Dagstuhl Castle, Germany (1999)Google Scholar
  8. 8.
    Hahn, U., Romacker, M.: Content management in the syndikate system: How technical documents are automatically transformed to text knowledge bases. Data & Knowledge Engineering 35(1), 137–159 (2000)CrossRefzbMATHGoogle Scholar
  9. 9.
    Harris, Z.: Methods in Structural Linguistics. University of Chicago Press, Chicago (1951)Google Scholar
  10. 10.
    Jaszuk, M., Szostek, G., Walczak, A.: An Ontology Building System for Structuring Medical Diagnostic Knowledge. In: 3rd Conference on Human System Interaction, pp. 203–210. Rzeszow (2010)Google Scholar
  11. 11.
    Maedche, A., Volz, R.: The ontology extraction & maintenance framework: Text-to-onto. In: Proceedings of the IEEE International Conference on Data Mining, California, USA (2001)Google Scholar
  12. 12.
    Oliveira, A., Pereira, F., Cardoso, A.: Automatic reading and learning from text. In: Proceedings of the International Symposium on Artificial Intelligence (ISAI), Kolhapur, India (2001)Google Scholar
  13. 13.
    Polański, K. (ed.): Słownik syntaktyczno-generatywny czasowników polskich, Kraków, vol. 1-7 (1908-1993)Google Scholar
  14. 14.
    Przepiórkowski, A.: The IPI PAN Corpus. Preliminary Version. Institute of Computer Science PAS, Warsaw (2004)Google Scholar
  15. 15.
    Szczeklik, A. (ed.): Choroby wewnȩtrzne. Medycyna praktyczna, Kraków (2006)Google Scholar
  16. 16.
    Szpakowicz, S.: Formalny opis składniowy zdań polskich. Wydawnictwa UW (1983)Google Scholar
  17. 17.
    Velardi, P., Navigli, R., Cucchiarelli, A., Neri, F.: Evaluation of ontolearn, a methodology for automatic learning of ontologies. In: Buitelaar, P., Cimmiano, P., Magnini, B. (eds.) Ontology Learning from Text: Methods, Evaluation and Applications. IOS Press, Amsterdam (2005)Google Scholar
  18. 18.
    Velardi, P., Fabriani, P., Missikoff, M.: Using text processing techniques to automatically enrich a domain ontology. In: Proceedings of the International Conference on Formal Ontology in Information Systems (FOIS), Ogunquit, Maine (2001)Google Scholar
  19. 19.
    Woliński, M.: System znaczników syntaktycznych w korpusie IPI PAN, Poloniki, vol. XXII/XXIII, pp. 39–55 (2003)Google Scholar
  20. 20.
    Woliński, M.: Morfeusz - a Practical Tool for the Morphological Analysis of Polish. In: Woliński, M. (ed.) Intelligent Information Processing and Web Mining, Advances in Soft Computing, vol. 35, pp. 511–520. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  21. 21.
    Ontology Online, (accessed November 27, 2010)

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Grażyna Szostek
    • 1
  • Marek Jaszuk
    • 1
    • 2
  1. 1.Information Systems InstituteMilitary University of TechnologyWarsawPoland
  2. 2.Programming DepartmentUniversity of Information Technology and ManagementRzeszówPoland

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