Knowledge Representation for the Intelligent Legal Case Retrieval

  • Yiming Zeng
  • Ruili Wang
  • John Zeleznikow
  • Elizabeth Kemp
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3681)


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.


Alternative Dispute Resolution Legal Case Legal Knowledge Legal Precedent Case Retrieval 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Prakken, H., Sartor, G.: Modelling Reasoning with Precedents in a Formal Dialogue Game. Artificial Intelligence and Law 6, 231–287 (1998)CrossRefGoogle Scholar
  2. 2.
    Stein, G.C.: Common-Sense Reasoning about Beliefs. Ph.D. Thesis. New Mexico State University (1996)Google Scholar
  3. 3.
    Porter, B.W., Bareiss, E.R., Holte, R.C.: Concept Learning and Heuristic Classification in Week-theory Domains. Artificial Intelligence 45, 1–2 (1990)CrossRefGoogle Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    Aleven, V.: Using Background Knowledge in Case-based Legal Reasoning: A Computational Model and an Intelligent Learning Environment. Artificial Intelligence 150, 183–237 (2003)zbMATHCrossRefGoogle Scholar
  9. 9.
    Waddams, S.M.: Introduction to the Study of Law, pp. 77–84. Carswell/ Thomson Professional Publishing, Ontario (1992)Google Scholar
  10. 10.
    Ashley, K.D.: Case-based Reasoning and Its Implications for Legal Expert Systems. Artificial Intelligence and Law 1(2), 113–208 (1992)CrossRefGoogle Scholar
  11. 11.
    Rissland, E.L., Ashley, K.: A Note on Dimensions and Factors. Artificial Intelligence and Law 10, 65–77 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yiming Zeng
    • 1
  • Ruili Wang
    • 1
  • John Zeleznikow
    • 2
  • Elizabeth Kemp
    • 1
  1. 1.Institute of Information Sciences and TechnologyMassey UniversityPalmerston NorthNew Zealand
  2. 2.School of Information SystemsVictoria UniversityMelbourne MCAustralia

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