eCite Digital Repository

Expert-driven knowledge discovery


Ling, TR and Kang, BH and Johns, DP and Walls, JT and Bindoff, IK, Expert-driven knowledge discovery, Proceedings of the 5th International Conference on Information Technology - New Generations, 7-9 April 2008, Las Vegas, Nevada, pp. 174-178. ISBN 978-0-7695-3099-4 (2008) [Refereed Conference Paper]

Copyright Statement

Copyright 2008 IEEE

DOI: doi:10.1109/ITNG.2008.194


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.

Item Details

Item Type:Refereed Conference Paper
Keywords:knowledge, discovery, data mining, knowledge acquisition, expert
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Knowledge representation and reasoning
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the information and computing sciences
UTAS Author:Ling, TR (Dr Tristan Ling)
UTAS Author:Kang, BH (Professor Byeong Kang)
UTAS Author:Johns, DP (Associate Professor David Johns)
UTAS Author:Walls, JT (Professor Justin Walls)
UTAS Author:Bindoff, IK (Dr Ivan Bindoff)
ID Code:54042
Year Published:2008
Web of Science® Times Cited:5
Deposited By:Information and Communication Technology
Deposited On:2009-02-09
Last Modified:2015-02-04

Repository Staff Only: item control page