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Simultaneous Modelling and Knowledge Acquisition Using NRDR

机译:使用NRDR同时进行建模和知识获取

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摘要

Incremental refinement methods of knowledge bases ease maintenance but fail to uncover the underlying domain model used by the expert. In this paper, we propose a new knowledge representation formalism for incremental acquisition and refinement of knowledge. It guides the expert in expressing his model of the domain during the actual knowledge acquisition process. This knowledge representation scheme, Nested Ripple Down Rules, is a substantial extension to Ripple Down Rule (RDR) knowledge acquisition framework. This paper introduces a theoretical framework for analysing the structure of RDR in general and NRDR in particular. Using this framework we analyse the conditions under which RDR converges towards the target knowledge base. Further, we analyse the conditions under which NRDR offers an effective approach for domain modelling. We discuss the maintenance problems of NRDR as a function of this convergence. We show that the maintenance of NRDR requires similar effort to maintaining RDR for most of the knowledge base development cycle. We show that when an NRDR knowledge base shows an increase in maintenance requirement in comparison with RDR during its development, this added requirement can be automatically handled.
机译:知识库的增量提炼方法易于维护,但无法揭示专家所使用的基础领域模型。在本文中,我们提出了一种新的知识表示形式,用于增量获取和完善知识。它指导专家在实际知识获取过程中表达自己的领域模型。这种知识表示方案,即嵌套纹波下降规则,是对纹波下降规则(RDR)知识获取框架的实质扩展。本文介绍了一个理论框架来分析RDR的总体结构,尤其是NRDR的结构。使用此框架,我们分析了RDR趋向目标知识库的条件。此外,我们分析了NRDR提供有效域建模方法的条件。我们将NRDR的维护问题作为这种收敛的函数进行讨论。我们表明,在大多数知识库开发周期中,NRDR的维护都需要类似的工作来维护RDR。我们显示,当NRDR知识库在开发过程中与RDR相比显示出维护要求的增加时,可以自动处理增加的要求。

著录项

  • 来源
    《》|1998年|83-95|共13页
  • 会议地点 Singapore(SG);Singapore(SG)
  • 作者单位

    School of Computer Sciences and Engineering University of New South Wales Sydney, NSW 2052, Australia;

    School of Computer Sciences and Engineering University of New South Wales Sydney, NSW 2052, Australia;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自动化系统理论;
  • 关键词

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