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Towards painless knowledge acquisition

  • Data Mining
  • Conference paper
  • First Online:
Advances in Knowledge Acquisition (EKAW 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1076))

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Abstract

This paper argues that there is a discernible trend in Knowledge Acquisition towards systems which are easier for the domain expert to use; such systems ask more focused questions and questions at a higher conceptual level. Two systems, REFINER+ and TIGON which illustrate this trend are described in some detail; these have been applied in the domains of patient management and diagnosis of turbine errors respectively. Other trends noted include:

  • Co-operative systems for Knowledge Acquisition/Problem Solving.

  • The re-use of existing knowledge(bases) Additionally, the relationship of the TIGON system to Data Mining is discussed; as is the inference of diagnostic rules for dynamic systems from the systems performance data.

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References

  • K D Ashley (1990) Modelling legal argument: Reasoning with cases and hypotheticals. Cambridge MA: MIT Press, Bradford Books

    Google Scholar 

  • P Clark & R Boswell (1991) Rule Induction with CN2: Some recent improvements. In Proceedings of EWSL-91, Y Kodratoff (ed.) Springer-Verlag pp463–481

    Google Scholar 

  • P Compton & R Jansen (1990) A Philosophical basis for knowledge acquisition. Knowledge Acquisition, 2, pp241–257

    Google Scholar 

  • D Diaper (1989) Knowledge Elicition: Principles, techniques and applications. Sussex: Ellis Horwood

    Google Scholar 

  • K A Ericsson & H A Simon (1984) Protocol Analysis. Cambridge, Mass: MIT Press.

    Google Scholar 

  • L Eshelman (1988). “MOLE: a knowledge-acquisition tool for cover-and-differentiate systems”. In S. Marcus (Ed.), Automating Knowledge Acquisition for Expert Systems. Norwood, Mass: Kluwer Academic, pp 37–80.

    Google Scholar 

  • L Fu (1994) Neural Networks in computer Intelligence. McGraw-Hill Inc.

    Google Scholar 

  • G Kahn (1988). MORE: From Observing Knowledge Engineers to Automating Knowledge Acquisition. In S. Marcus (Ed.) Automating Knowledge acquisition for Expert Systems. Norwell, Mass: Kluwer Academic, pp 7–35.

    Google Scholar 

  • B King, A P Steward & J I Tait (1995). Towards Automated Knowledge Acquisition for Process Plant Diagnosis. IEE Colloquium on KDD, Digest No 1995/021 (B), February 1995.

    Google Scholar 

  • Y Kodratoff & G Tecuci (1987) What is an example in DISCIPLE? In Proceedings of the 4th International ML workshop, Uni. Of California, Irvine. Publ: Morgan-Kaufmann pp 160–166

    Google Scholar 

  • P Langley, H A Simon, G Bradshaw & J M Zytkow (1987) Scientific Discovery: An account of the creative Process. Cambridge, Mass. MIT Press

    Google Scholar 

  • P Leo, D Sleeman & A Tsinakos (1994). S-SALT: A problem solver plus knowledge acquisition tool which additionally can refine its knowledge base. In Proceeding EKAW-94.

    Google Scholar 

  • S Marcus (1988). A knowledge acquisition tool for propose-and-revise systems. In S Marcus (Ed), Automating Knowledge Acquisition for Expert Systems. Boston: Kluwer Academic, pp 81–123.

    Google Scholar 

  • R Milne (1993). ESPRIT TIGER project, Knowledge Acquisition Summary — FEP.

    Google Scholar 

  • R Milne T Gausch (1994) Automatic Diagnostic Development on a programmable logic controller. In Applications & Innovations in Expert Systems 11. (R Milne & A Montgomery eds.) Oxford: Information Press, p99–110.

    Google Scholar 

  • F Mitchell, D Sleeman & R Milne, (1995) KA the KDD way or How to do Knowledge Acquisition without completely annoying your expert. IEE Colloquium on KDD, Digest No 1995/021 (B), February 1995.

    Google Scholar 

  • T M Mitchell R M Keller & S T Kedar-Cabelli (1986) Explanation-based generalisation: A unifying view. Machine Learning I, I, pp 47–80

    Google Scholar 

  • M A Musen (1990) An editor for the conceptual models of interactive knowledge-acquisition tools. In The Foundations of Knowledge Acquisition, J Boose & B G Gaines (eds) London: Academic Press, pp 135–160

    Google Scholar 

  • R Neches, R Fikes, T Finin, T Gruber, T Senator & W Swartout, (1991) Enabling technology for knowledge sharing. AI Magazine, 12, pp 36–56

    Google Scholar 

  • J Penman & C M Yin (1994) Feasibility of using unsupervised learning, artificial neural networks for the condition monitoring of electrical machines. In IEE Proceedings of Electr. Power Appl., Vol 141 No 6.

    Google Scholar 

  • G Piatetsky-Shapiro & W J Frawley (eds.) (1991) Knowledge Discovery in Databases. AAAI Press

    Google Scholar 

  • G Piatetsky-Shapiro & C J Matheus (1992) Knowledge Discovery Workbench for Exploring Business Databases. International Journal of Intelligent Systems, Vol. 7, No. 7, September 1992.

    Google Scholar 

  • J R Quinlan (1986) Induction of decision trees. Machine Intelligence, I pp81–106

    Google Scholar 

  • J R Quinlan (1993) C4.5 Programs for Machine Learning. Publ: San Mateo, CA:Morgan Kaufmann

    Google Scholar 

  • H Reichgelt & N Shadbolt (1992). PROTOKEW: A knowledge based system for knowledge acquisition. In: Artificial Intelligence Research Directions in Cognitive Science, D Sleeman & N O Berson, (eds.) Publ Hove: LEA, pp 171–202.

    Google Scholar 

  • Rolls-Royce plc, (1986) The Jet engine, Rolls-Royce plc.

    Google Scholar 

  • S Sharma & D Sleeman (1988) Refiner: A Case-based Differential Diagnosis Aide for Knowledge Acquisition and Knowledge Refinement. In Proceedings of EWSL-88, D Sleeman (eds.) Pitman: London, pp201–210.

    Google Scholar 

  • D Sleeman (1994) Some Recent Advances in KB Systems Building. Research and Development in Expert Systems XI, Proceedings of Expert Systems 94, Oxford: Information Press, p3–16

    Google Scholar 

  • G G Towell & J W Shavlik (1994) Refining symbolic knowledge using neural networks. Machine Learning IV, R Michalski & G Tecuci (eds) pp405–429

    Google Scholar 

  • M Winter & D Sleeman (1995). REFINER+: An Efficient System for Detecting and Removing Inconsistencies in Example Sets. In Research & Development in Expert Systems XII M A Bramer J L Nealon & R Milne (eds). Oxford: Information Press, pp 115–132.

    Google Scholar 

  • J M Zytkow & J Baker (1991) Interactive Mining of Regularities in Database In Knowledge Discovery in Databases, G Piatetsky-Shapiro & W J Frawley (eds.) AAAI Press.

    Google Scholar 

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Authors and Affiliations

  1. Department of Computing Science King's College, The University, AB9 2UE, Aberdeen, Scotland, UK

    Derek Sleeman & Fraser Mitchell

Authors
  1. Derek Sleeman
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  2. Fraser Mitchell
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Editor information

Nigel Shadbolt Kieron O'Hara Guus Schreiber

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© 1996 Springer-Verlag Berlin Heidelberg

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Sleeman, D., Mitchell, F. (1996). Towards painless knowledge acquisition. In: Shadbolt, N., O'Hara, K., Schreiber, G. (eds) Advances in Knowledge Acquisition. EKAW 1996. Lecture Notes in Computer Science, vol 1076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61273-4_17

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  • DOI: https://doi.org/10.1007/3-540-61273-4_17

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61273-5

  • Online ISBN: 978-3-540-68391-9

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