September 10, 1998

John Zeleznikow
Applied Computing Research Institute, La Trobe University, AUSTRALIA

Knowledge Discovery and Machine Learning in the Legal Domain

Whilst cases are of great significance in Common Law, there has been minimal research about knowledge discovery in the legal domain. We claim for knowledge discovery to be feasible, the domain must contain an abundance of commonplace cases.

We discuss in detail a hybrid rule-based/neural network system, Split_Up which provides advice upon the distribution of property following divorce in Australia. Explanations in Split_Up are provided using the argumentation theory of Stephen Toulmin.

We also discuss an in detail evaluation of the Split Up system.

Dr. John Zeleznikow is the author of more than eighty refereed articles and has recently written the book: Zeleznikow, J. and Hunter, D., 1994, Building Intelligent Legal Information Systems: Knowledge Representation and Reasoning in Law, Kluwer Computer/Law series. He was general chairman of the sixth International Conference on Artificial Intelligence and Law held at the University of Melbourne in July 1997.

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