KR4IPLaw Judgment Miner - Case-Law Mining for Legal Norm Annotation

  • Shashishekar RamakrishnaEmail author
  • Łukasz Górski
  • Adrian Paschke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10791)


The use of pragmatics in applying the law is hard to deal with for a legal knowledge engineer who needs to model it in a precise KR for (semi-)automated legal reasoning systems. The negative aspects of pragmatics is due to the difficulty involved in separating their concerns. When representing a legal norm for (semi-)automated reasoning, an important step/aspect is the annotation of legal sections under consideration. Annotation in the context of this paper refers to identification, segregation and thereafter representation of the content and its associated context. In this paper we present an approach and provide a proof-of-concept implementation for automatizing the process of identifying the most relevant judgment pertaining to a legal section and further transforming them into a formal representation format. The annotated legal section and its related judgments can then be mapped into a decision model for further down the line processing.


LegalDocML Case-law mining Legal norms Topic modeling 


  1. 1.
    Palmirani, M., Governatori, G., Rotolo, A., Tabet, S., Boley, H., Paschke, A.: LegalRuleML: XML-based rules and norms. In: Olken, F., Palmirani, M., Sottara, D. (eds.) RuleML 2011. LNCS, vol. 7018, pp. 298–312. Springer, Heidelberg (2011). Scholar
  2. 2.
    Athan, T., Boley, H., Governatori, G., Palmirani, M., Paschke, A., Wyner, A.: OASIS LegalRuleML. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law, pp. 3–12. ACM (2013)Google Scholar
  3. 3.
    Hoekstra, R., et al.: The LKIF core ontology of basic legal concepts. LOAIT 321, 43–63 (2007)Google Scholar
  4. 4.
    Palmirani, M., Vitali, F.: Akoma Ntoso an open document standard for parliaments (2014)Google Scholar
  5. 5.
    Vitali, F., Zeni, F.: Towards a country-independent data format: the Akoma Ntoso experience. In: Proceedings of the V legislative XML Workshop, Florence, Italy, pp. 67–86. European Press Academic Publishing (2007)Google Scholar
  6. 6.
    Boley, H., et al.: Design rationale for RuleML: a markup language for semantic web rules. In: SWWS, vol. 1, pp. 381–401 (2001)Google Scholar
  7. 7.
    Lee, D.L., Chuang, H., Seamons, K.: Document ranking and the vector-space model. IEEE Softw. 14(2), 67–75 (1997)CrossRefGoogle Scholar
  8. 8.
    Maxwell, K.T., Schafer, B.: Concept and context in legal information retrieval. In: JURIX, pp. 63–72 (2008)Google Scholar
  9. 9.
    Jackson, P., Al-Kofahi, K., Tyrrell, A., Vachher, A.: Information extraction from case law and retrieval of prior cases. Artif. Intell. 150(1), 239–290 (2003)CrossRefGoogle Scholar
  10. 10.
    Wyner, A., Mochales-Palau, R., Moens, M.-F., Milward, D.: Approaches to text mining arguments from legal cases. In: Francesconi, E., Montemagni, S., Peters, W., Tiscornia, D. (eds.) Semantic Processing of Legal Texts. LNCS (LNAI), vol. 6036, pp. 60–79. Springer, Heidelberg (2010). Scholar
  11. 11.
    Ashley, K.D., Walker, V.R.: From information retrieval (IR) to argument retrieval (AR) for legal cases: report on a baseline study. In: Legal Knowledge and Information Systems. IOS Press (2013)Google Scholar
  12. 12.
    Firdhous, M.: Automating legal research through data mining. arXiv preprint arXiv:1211.1861 (2012)
  13. 13.
    Ashley, K., Brninghaus, S.: Automatically classifying case texts and predicting outcomes. Artif. Intell. Law 17(2), 125–165 (2009)CrossRefGoogle Scholar
  14. 14.
    Fuller, L.L.: The Morality of Law, vol. 152. Yale University Press, New Haven (1977)Google Scholar
  15. 15.
    Marmor, A.: The pragmatics of legal language. Ratio Juris 21(4), 423–452 (2008)CrossRefGoogle Scholar
  16. 16.
    Benjamins, V.R., Contreras, J., Casanovas, P., Ayuso, M., Becue, M., Lemus, L., Urios, C.: Ontologies of professional legal knowledge as the basis for intelligent it support for judges. Artif. Intell. Law 12(4), 359–378 (2004)CrossRefGoogle Scholar
  17. 17.
    Ramakrishna, S., Gorski, L., Paschke, A.: The role of pragmatics in legal norm representation. CoRR abs/1507.02086 (2015)Google Scholar
  18. 18.
    OMG: Semantics of Business Vocabulary and Business Rules (SBVR) v. 1.3 (2015)Google Scholar
  19. 19.
    Bézivin, J., Gerbé, O.: Towards a precise definition of the OMG/MDA framework. In: Proceedings of 16th Annual International Conference on Automated Software Engineering, 2001 (ASE 2001), pp. 273–280. IEEE (2001)Google Scholar
  20. 20.
    Kozlenkov, A., Paschke, A.: Prova rule language version 3.0 user’s guide. (2010)
  21. 21.
    Jeff, A., Stephen, G.: The minion search engine: indexing, search, text similarity and tag gardening. Technical report, Sun Microsystems, New York (2008)Google Scholar
  22. 22.
    Robertson, S., Zaragoza, H.: The Probabilistic Relevance Framework: BM25 and Beyond. Now Publishers Inc., Breda (2009)Google Scholar
  23. 23.
    Porter, M.F.: Snowball: a language for stemming algorithms (2001)Google Scholar
  24. 24.
    Newman, D., Asuncion, A., Smyth, P., Welling, M.: Distributed algorithms for topic models. J. Mach. Learn. Res. 10, 1801–1828 (2009)MathSciNetzbMATHGoogle Scholar
  25. 25.
    Ramakrishna, S.: First approaches on knowledge representation of elementary (patent) pragmatics. In: Joint Proceedings of the 7th International Rule Challenge, the Special Track on Human Language Technology and the 3rd RuleML Doctoral Consortium (2013)Google Scholar
  26. 26.
    Rissland, E.L., Ashley, K.D., Branting, L.K.: Case-based reasoning and law. Knowl. Eng. Rev. 20(03), 293–298 (2005)CrossRefGoogle Scholar
  27. 27.
    Ramakrishna, S., Paschke, A.: Bridging the gap between legal practitioners and knowledge engineers using semi-formal KR. In: The 8th International Workshop on Value Modeling and Business Ontology, VMBO, Berlin (2014)Google Scholar
  28. 28.
    Ramakrishna, S., Paschke, A.: Semi-automated vocabulary building for structured legal english. In: Bikakis, A., Fodor, P., Roman, D. (eds.) RuleML 2014. LNCS, vol. 8620, pp. 201–215. Springer, Cham (2014). Scholar
  29. 29.
    Boley, H., Paschke, A., Shafiq, O.: RuleML 1.0: the overarching specification of web rules. In: Dean, M., Hall, J., Rotolo, A., Tabet, S. (eds.) RuleML 2010. LNCS, vol. 6403, pp. 162–178. Springer, Heidelberg (2010). Scholar
  30. 30.
    Paschke, A.: Reaction RuleML 1.0 for rules, events and actions in semantic complex event processing. In: Bikakis, A., Fodor, P., Roman, D. (eds.) RuleML 2014. LNCS, vol. 8620, pp. 1–21. Springer, Cham (2014). Scholar
  31. 31.
    Ramakrishna, S., Paschke, A.: A process for knowledge transformation and knowledge representation of patent law. In: Bikakis, A., Fodor, P., Roman, D. (eds.) RuleML 2014. LNCS, vol. 8620, pp. 311–328. Springer, Cham (2014). Scholar
  32. 32.
    Paschke, A., Ramakrishna, S.: Legal RuleML Tutorial Use Case - LegalRuleML for Legal Reasoning in Patent Law (2013)Google Scholar
  33. 33.
    Ramakrishna, S., Gorski, Ł., Paschke, A.: A dialogue between a lawyer and computer scientist: the evaluation of knowledge transformation from legal text to computer-readable format. Appl. Artif. Intell. 30(3), 216–232 (2016)CrossRefGoogle Scholar
  34. 34.
    Bernstam, E.V., Herskovic, J.R., Aphinyanaphongs, Y., Aliferis, C.F., Sriram, M.G., Hersh, W.R.: Using citation data to improve retrieval from MEDLINE. J. Am. Med. Inform. Assoc. 13(1), 96–105 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Shashishekar Ramakrishna
    • 1
    Email author
  • Łukasz Górski
    • 2
  • Adrian Paschke
    • 1
  1. 1.Freie Universität BerlinBerlinGermany
  2. 2.Nicolaus Copernicus UniversityToruńPoland

Personalised recommendations