Expert-Driven Knowledge Discovery

  • Authors:
  • Tristan Ling;Byeong Ho Kang;David P. Johns;Justin Walls;Ivan Bindoff

  • Affiliations:
  • -;-;-;-;-

  • Venue:
  • ITNG '08 Proceedings of the Fifth International Conference on Information Technology: New Generations
  • Year:
  • 2008

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Abstract

Knowledge Discovery techniques find new knowledge about a domain by analysing existing domain knowledge and examples of domain data. These techniques typically involve using a human expert and automated software analysis (Data Mining). Often the human expertise is used initially to choose which data is processed, and then finally to determine which results are relevant. However studies have noted that some domains contain data stores too extensive and detailed, and existing knowledge too complex, for effective data selection or efficient Data Mining. A different approach is suggested which involves the human expert more pervasively, taking advantage of their expertise at each step, while using Data Mining techniques to assist in discovering data trends and in verifying the expert’s findings. Preliminary results suggest that the approach can be successfully applied to discover new knowledge in a complex domain, and reveal many potential areas for research and development.