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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Prakken, H., Sartor, G.: Modelling Reasoning with Precedents in a Formal Dialogue Game. Artificial Intelligence and Law 6, 231–287 (1998)
Stein, G.C.: Common-Sense Reasoning about Beliefs. Ph.D. Thesis. New Mexico State University (1996)
Porter, B.W., Bareiss, E.R., Holte, R.C.: Concept Learning and Heuristic Classification in Week-theory Domains. Artificial Intelligence 45, 1–2 (1990)
Oskamp, A., Tragter, M.W., Lodder, A.R.: Mutual Benefits for AI & Law and Knowledge Management. In: Proceedings of the 7th International Conference on Artificial Intelligence and Law (ICAIL 1999), pp. 126–127 (1999)
Zeleznikow, J.: Using an Argumentation Based Approach to Manage Legal Knowledge. In: Schwartz, D.G. (ed.) Encyclopedia of Knowledge Management, Idea Group Inc, Hershey PA (2005) (in press)
Zeleznikow, J., Stranieri, A., Hunter, D.: Beyond Rule Based Reasoning. the Meaning and Use of Cases. In: Proceedings of the 11th Conference on Artificial Intelligence for Applications (1995)
Ashley, K.D., Rissland, E.L.: Waiting on Weighting: A Symbolic Least Commitment Approach. In: Proceedings of AAAI 1988, pp. 239–244. AAAI Press/MIT Press, Cambridge, MA (1988)
Aleven, V.: Using Background Knowledge in Case-based Legal Reasoning: A Computational Model and an Intelligent Learning Environment. Artificial Intelligence 150, 183–237 (2003)
Waddams, S.M.: Introduction to the Study of Law, pp. 77–84. Carswell/ Thomson Professional Publishing, Ontario (1992)
Ashley, K.D.: Case-based Reasoning and Its Implications for Legal Expert Systems. Artificial Intelligence and Law 1(2), 113–208 (1992)
Rissland, E.L., Ashley, K.: A Note on Dimensions and Factors. Artificial Intelligence and Law 10, 65–77 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zeng, Y., Wang, R., Zeleznikow, J., Kemp, E. (2005). Knowledge Representation for the Intelligent Legal Case Retrieval. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552413_49
Download citation
DOI: https://doi.org/10.1007/11552413_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28894-7
Online ISBN: 978-3-540-31983-2
eBook Packages: Computer ScienceComputer Science (R0)