Artificial Intelligence and Law

, Volume 17, Issue 2, pp 101–124 | Cite as

Improving legal information retrieval using an ontological framework

  • M. SaravananEmail author
  • B. Ravindran
  • S. Raman


A variety of legal documents are increasingly being made available in electronic format. Automatic Information Search and Retrieval algorithms play a key role in enabling efficient access to such digitized documents. Although keyword-based search is the traditional method used for text retrieval, they perform poorly when literal term matching is done for query processing, due to synonymy and ambivalence of words. To overcome these drawbacks, an ontological framework to enhance the user’s query for retrieval of truly relevant legal judgments has been proposed in this paper. Ontologies ensure efficient retrieval by enabling inferences based on domain knowledge, which is gathered during the construction of the knowledge base. Empirical results demonstrate that ontology-based searches generate significantly better results than traditional search methods.


Information retrieval Knowledge base Legal ontology Query enhancement 


  1. Bench-Capon TJM, Visser PRS (1997) Ontologies in legal information systems; the need for explicit specifications of domain conceptualizations. In: Proceedings of international conference on artificial intelligence and law (ICAIL-97), Melbourne, pp 132–141Google Scholar
  2. Biébow B, Szulman S (1999) TERMINAE: a linguistics-based tool for building of a domain ontology In: Fensel D, Studer R (eds) Proceedings of the 11th European Workshop (EKAW’99), LNAI 1621, Springer-Verlag, pp 49–66Google Scholar
  3. Breuker BJ, Winckels R (2003) Use and reuse of legal ontologies in knowledge engineering and information management. In: Proceedings of the workshop on legal ontologies & web based legal information management (ICAIL 2003)Google Scholar
  4. Buckley A, Singhal A, Mitra M, Salton G (1998) New retrieval approaches using SMART. In: Proceedings of TREC-4, pp 25–48Google Scholar
  5. Burger J (2001) Issues, tasks and program structures to roadmap research in question & answering (Q&A), NIST Roadmap DocumentGoogle Scholar
  6. Daumé H III, Marcu D (2006). Bayesian query-focused summarization. In: Proceedings of the 21st international conference on computational linguistics and the 44th annual meeting of the ACL, July 17–18, Sydney, pp 305–312Google Scholar
  7. Enterprise search from microsoft empower people to find information and expertise, a microsoft white paper,
  8. Gangemi A, Prisco A, Sagri MT, Steve G, Tiscornia D (2003) Some ontological tools to support legal regulatory compliance, with a case study. In: Workshop WORM Core, LNCS, Springer Verlag, pp 607–620Google Scholar
  9. Gruber TR (1995) Towards principles for the design of ontologies used for knowledge sharing. Int J Hum Comput Stud 43:907–928. doi: 10.1006/ijhc.1995.1081 CrossRefGoogle Scholar
  10. Guarino N (1988). Formal ontology and information systems. In: Proceedings of FOIS’98, Trento, Italy, pp 3–15Google Scholar
  11. Hohfeld W (1996) Fundamental legal conceptions as applied in legal reasoning. Yale University Press, LondonGoogle Scholar
  12. Hotho A, Staab S, Stumme G (2003) Ontologies improve text document clustering. In: Proceedings of IEEE international conference on data mining (ICDM 2003), pp 541–544Google Scholar
  13. Johnson RA, Miller I, Freund J (1994) Probability and statistics for engineers, 5th edn. Prentice Hall of India, New DelhiGoogle Scholar
  14. Jones KS, Galliers JR (1996) Evaluating natural language processing review. Springer, New YorkGoogle Scholar
  15. Jones DM, Bench-Capon TJM, Visser PRS (1998) Methodologies for ontology development. In: Cuena J (ed) Proceedings of XV IFIP world computer congress, IT and knows, pp 62–75Google Scholar
  16. Kurland O (2006) Inter-document similarities—language models, and ad-hoc information retrieval, Ph.D. ThesisGoogle Scholar
  17. Lame G (2001) Constructing an IR-oriented legal ontology. In: Proceedings of the Second International Workshop on Legal Ontologies, 10th international conference on legal information Retrieval System, JURIX 2001, Amsterdam, pp 31–36Google Scholar
  18. Mani I, House D, Klein G, Hirschman L, Orbsl L, Firmin T, Chrzanowski M, Sundheirm B (1998) The TIPSTER SUMMAC text summarization evaluation, MITRE Technical report, MTR98W0000138, The MITRE CorporationGoogle Scholar
  19. Moens M (1999) Automatic indexing and abstracting of document texts. Kluwer Academic Publications, LondonGoogle Scholar
  20. Prieto-Diaz R (2003) A faceted approach to building ontologies. In: Proceedings of IEEE international conference on Information Reuse and Integration, IRIGoogle Scholar
  21. Saravanan M, Ravindran B, Raman S (2006a) Improving legal document summarization using graphical models. In: Proceedings of 19th international annual conference on legal knowledge and information systems, JURIX 2006, Paris, France, pp 51–60Google Scholar
  22. Saravanan M, Raman S, Ravindran B (2006b) A probabilistic approach to multi-document summarization for generating a tiled summary. Int J Comput Intell Appl 6(2):231–243. doi: 10.1142/S1469026806001976 CrossRefGoogle Scholar
  23. Siegal S, Castellan NJ (1988) Nonparametric statistics for the behavioral sciences. McGraw Hill, BerkeleyGoogle Scholar
  24. Skuce D, Monarch I (1990). Ontological issues in knowledge base design: some problems and suggestions. Fifth knowledge acquisition for knowledge based systems workshop, BanffGoogle Scholar
  25. Valente A (1995) Legal knowledge engineering: a modeling approach. IOS Press, AmsterdamGoogle Scholar
  26. Valente A, Breuker J (1994) Making ends meet: conceptual models and ontologies in legal problem solving. In: Proceedings of the XI Brazilian AI Symposium (SBIA’94), pp 1–15Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIIT MadrasChennaiIndia

Personalised recommendations