Knowledge representation for the intelligent legal case retrieval

Authors:
Yiming Zeng;Ruili Wang;John Zeleznikow;Elizabeth Kemp
Affiliations:
Institute of Information Sciences and Technology, Massey University, Palmerston North, New Zealand;Institute of Information Sciences and Technology, Massey University, Palmerston North, New Zealand;School of Information Systems, Victoria University, Melbourne MC, Victoria, Australia;Institute of Information Sciences and Technology, Massey University, Palmerston North, New Zealand
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2005

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Abstract

In this paper, we develop a knowledge representation model for the intelligent retrieval of legal cases, which provides effective legal case management. Examples are taken from the domain of accident compensation. A new set of sub-elements for legal case representation has been developed to extend the traditional representation elements of issues and factors. In our model, an issue may need to be further decomposed into sub-issues, and factors are categorized into pro-claimant, pro-responder and neutral factors. These extensions can effectively reveal the factual relevance between legal cases. Based on the knowledge representation model, we propose the IPN algorithm for intelligent legal case retrieval. Experiments and statistical analysis have been conducted to demonstrate the effectiveness of the proposed representation model and the IPN algorithm.