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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.

Keywords

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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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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