Text Mining for Real-time Ontology Evolution

  • Jackei H. K. Wong
  • Tharam S. Dillon
  • Allan K. Y. Wong
  • Wilfred W. K. Lin

In this paper we propose the novel technique, On-line Contin-uous Ontological Evolution (OCOE) approach, which applies text mining to automate TCM (Traditional Chinese Medicine) telemedicine ontology evolution. The first step of the automation process is opening up a closed skeletal TCM ontology core (TCM onto-core) for continuous evolution and absorption of new scientific knowledge. The test-bed for the OCOE verification was the production TCM telemedicine system of the Nong‘s Company Limited; Nong’s is a subsidiary of the PuraPharm Group in the Hong Kong SAR, which is dedicated to TCM telemedicine system development. At Nong's the skeletal TCM onto-core for clinical practice is closed (does not automatically evolve). When the OCOE is combined with the Nong's enterprise TCM onto-core it: i) invokes its text miner by default to search for new scientific findings over the open web incessantly; and ii) selectively prunes and stores useful new findings in special OCOE data structures. These data structures can be appended to the skeletal TCM onto-core logically to catalyze the evolution of the overall system TCM ontology, which is the logical combination: “original skeletal TCM onto-core plus contents of the special OCOE data structures” The evolutionary process works on the contents of the OCOE data structures only and does not alter any skeletal TCM onto-core knowledge. This onto-core evolution approach is called the “logical-knowledge-add-on” technique. OCOE deactivation will nullify the pointers and cut the association between the OCOE data structures and skeletal TCM onto-core, thus immediately reverting the clinical practice back to the original skeletal TCM onto-core basis.


Text Mining Common Cold Primary Attribute Telemedicine System Secondary Attribute 
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.
    A. Lacroix, L. Lareng, G. Rossignol, D. Padeken, M. Bracale, Y. Ogushi, R. Wootton, J.Sanders, S. Preost, and I. McDonald, G-7 Global Healthcare Applications Sub-project 4,Telemedicine Journal, March 1999Google Scholar
  2. 2.
    Wilfred W. K. Lin, Jackei H.K. Wong and Allan K.Y. Wong, Applying Dynamic Buffer Tuning to Help Pervasive Medical Consultation Succeed, the 1stInternational Workshop on Pervasive Digital Healthcare (PerCare) in the 6thIEEE International Conference on Pervasive Computing and Communications (Percom2008), Hong Kong March, 2008, Hong KongGoogle Scholar
  3. 3.
    Allan K.Y. Wong, Tharam S. Dillon and Wilfred W.K. Lin, Harnessing the Service Roundtrip Time Over the Internet to Support Time-Critical Applications — Concepts, Techniques and Cases, Nova Science Publishers, New York, 2008Google Scholar
  4. 4.
    R. Rifaieh and A. Benharkat, From Ontology Phobia to Contextual Ontology Use in Enterprise Information System, in Web Semantics & Ontology, ed. D. Taniar and J. Rahayu, Idea Group Inc., 2006Google Scholar
  5. 5.
    T. R. Gruber, A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition, 5(2), 1993, 199–220CrossRefGoogle Scholar
  6. 6.
    N. Guarino, P. Giaretta, Ontologies and Knowledge Bases: Towards a Terminological Clarification. In Towards very large knowledge bases: Knowledge building and knowledge sharing,1995, Amsterdam, The Netherlands: ISO Press, 25–32Google Scholar
  7. 7.
    L.E. Holzman, T.A. Fisher, L.M. Galitsky, A. Kontostathis, W.M. Pottenger, A Software Infrastructure for Research in Textual Data Mining, The International Journal on Artificial Intelligence Tools, 14(4), 2004, 829–849CrossRefGoogle Scholar
  8. 8.
    S. Bloehdorn, P. Cimiano, A. Hotho and S. Staab, An Ontology-based Framework for Text Mining. LDV Forum - GLDV Journal for Computational Linguistics and Language Technology, 20(1), 2005, 87–112Google Scholar
  9. 9.
    M.S. Chen, S.P. Jong and P.S. Yu, Data Mining for Path Traversal Patterns in a Web Environment, Proc. of the 16th.International Conference on Distributed Computing Systems, May 1996, Hong Kong, 385–392Google Scholar
  10. 10.
    U.M. Fayyad, G.Piatesky-Shapiro, P. Smyth and R. Uthurusamy, Advances in Knowledge Discovery and Data Mining, AAAI Press/MIT Press, 1996Google Scholar
  11. 11.
    W. Pedrycz and F. Gomide, An Introduction to Fuzzy Sets: Analysis and Design, MIT Press,1998Google Scholar
  12. 12.
    C.M. van der Walt and E. Barnard, Data Characteristics that Determine Classifier Performance, Proc. of the 16th Annual Symposium of the Pattern Recognition Association of South Africa, 2006, 160–165Google Scholar
  13. 13.
    R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules, Proc. of the 20thConference on Very Large Databases, Santiago, Chile, September 1994Google Scholar
  14. 14.
    F. Yu, Collaborative Web Information Retrieval and Extraction - Implementation of an Intelligent System “LightCamel”, BAC Final Year Project, Department of Computing, Hong Kong Polytechnic University, Hong Kong SAR, 2006Google Scholar
  15. 15.
    M. Ou, Chinese-English Dictionary of Traditional Chinese Medicine, C & C Joint Printing Co. (H.K.) Ltd., ISBN 962-04-0207-3, 1988Google Scholar
  16. 16.

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Jackei H. K. Wong
    • 1
  • Tharam S. Dillon
    • 2
  • Allan K. Y. Wong
    • 1
  • Wilfred W. K. Lin
    • 1
  1. 1.Department of ComputingHong Kong Polytechnic UniversityHong Kong SAR
  2. 2.Digital Ecosystems and Business Intelligence InstituteCurtin University of TechnologyPerthWestern Australia

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