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International Conference on Knowledge Engineering and Knowledge Management

EKAW 2004: Engineering Knowledge in the Age of the Semantic Web pp 293–306Cite as

Invented Predicates to Reduce Knowledge Acquisition

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3257))

Abstract

The aim of this study was to develop machine-learning techniques that would speed up knowledge acquisition from an expert. As the expert provided knowledge the system would generalize from this knowledge in order to reduce the need for later knowledge acquisition. This generalization should be completely hidden from the expert. We have developed such a learning technique based on Duce’s intra-construction and absorption operators [1] and applied to Ripple-Down Rule (RDR) incremental knowledge acquisition [2]. Preliminary evaluation shows that knowledge acquisition can be reduced by up to 50%.

This is a modified version of paper presented at the IJCAI 2003 Workshop on Mixed-Initiative Intelligent Systems

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

    Hendra Suryanto & Paul Compton

Authors
  1. Hendra Suryanto
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  2. Paul Compton
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Editor information

Editors and Affiliations

  1. Knowledge Media Institute, The Open University, Milton Keynes, UK

    Enrico Motta

  2. Electronics and Computer Science, University of Southampton, UK

    Nigel R Shadbolt

  3. Knowledge Media Institute, Open University, UK

    Arthur Stutt

  4. University of Southampton, SO17 1BJ, Southampton, UK

    Nick Gibbins

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© 2004 Springer-Verlag Berlin Heidelberg

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Suryanto, H., Compton, P. (2004). Invented Predicates to Reduce Knowledge Acquisition. In: Motta, E., Shadbolt, N.R., Stutt, A., Gibbins, N. (eds) Engineering Knowledge in the Age of the Semantic Web. EKAW 2004. Lecture Notes in Computer Science(), vol 3257. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30202-5_20

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  • DOI: https://doi.org/10.1007/978-3-540-30202-5_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23340-4

  • Online ISBN: 978-3-540-30202-5

  • eBook Packages: Springer Book Archive

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