Abstract
The Split Up project applies knowledge discovery techniques (KDD) to legal domains. Theories of jurisprudence underpin a classification scheme that is used to identify tasks suited to KDD. Theoretical perspectives also guide the selection of cases appropriate for a KDD exercise. Further, jurisprudence underpins strategies for dealing with contradictory data. Argumentation theory is instrumental for representing domain expertise so that the KDD process can be constrained. Specifically, a variant of the argumentation structure proposed by Toulmin is used to decompose tasks into independent sub-tasks in the data transformation phase. This enables a complex KDD exercise to be decomposed into numerous simpler exercises that each require less data and have fewer instances of missing values. The use of the structure also facilitates the development of information systems that integrate multiple reasoning mechanisms such as first order logic, neural networks or fuzzy inferences and provides a convenient structure for the generation of explanations. The viability of this approach was tested with the development of a system that predicts property split outcomes in cases litigated in the Family Court of Australia. The system has been evaluated using a mix of strategies that derive from a framework proposed by Reich.
Recommended Citation
Stranieri, Andrew and Zeleznikow, John, "Knowledge Discovery for Decision Support in Law" (2000). ICIS 2000 Proceedings. 68.
https://aisel.aisnet.org/icis2000/68