Knowledge discovery in discretionary legal domains

  • John Zeleznikow
  • Andrew Stranieri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1394)


Significant obstacles must be overcome if knowledge discovery techniques are to be applied in the legal domain. In this paper we argue that in order to use knowledge discovery in the legal domain it is essential to use domain expertise and important that an abundance of commonplace cases is available.

Even with appropriate data, data mining techniques in law must deal with contradictory cases and use statistical techniques in order to define error and estimate performance. We illustrate these points by describing our own error heuristic and the method we use for dealing with contradictions for the training of neural networks in the domain of property proceedings in Australian Family Law. In law, an explanation for a decision reached is often more important than the decision. We advocate the use of a theory of argumentation developed by the British philosopher Stephen Toulmin to provide explanations to support the outcomes predicted by our knowledge discovery system Split Up. We also discuss the use genetic algorithms to minimise the number of features our knowledge discovery system must use.


Knowledge Discovery in Legal Domains Neural Networks Explanation Feature Selection 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • John Zeleznikow
    • 1
  • Andrew Stranieri
    • 2
  1. 1.Database Research Laboratory, Applied Computing Research InstituteBundooraAustralia
  2. 2.School of Information Technology and Mathematical SciencesUniversity of BallaratBallaratAustralia

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