A Support System for the Analysis and the Management of Complex Ruling Documents

  • Marco Bianchi
  • Mauro Draoli
  • Giorgio Gambosi
  • Giovanni Stilo
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 37)


This paper reports the experiment conducted for the development and assessment of a new software tool allowing the automatic discovery of correlations in large legislative frameworks.

The system, named NavigaNorme, has been mainly designed to support experts of the Legal domain involved in the simplification of the Italian normative framework for all levels of the Public Administration. In fact, the most relevant functionality in NavigaNorme is the identification, given a paragraph in a selected norm, of those paragraphs (in the same norm and in other norms) that should be considered in trying to reduce the number of norms being in force or in drafting a new law.

NavigaNorme relies on a search engine that combines classical Information retrieval techniques with some ad-hoc strategies introduced to increase the precision of the retrieval by exploiting implicit information extracted from the logical structure of Legal and normative texts.

The effectiveness of NavigaNorme has been mainly measured in terms of precision, through an assessment procedure that involved experts of the Legal domain.


Normative texts analysis Information retrieval Evaluation of search engines 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    CNIPA: Formato per la rappresentazione elettronica dei provvedimenti normativi tramite il linguaggio di marcatura XML. TR: AIPA-CR-40 (2002)Google Scholar
  2. 2.
  3. 3.
    Amati, G.: Frequentist and Bayesian Approach to Information Retrieval. In: Lalmas, M., MacFarlane, A., Rüger, S.M., Tombros, A., Tsikrika, T., Yavlinsky, A. (eds.) ECIR 2006. LNCS, vol. 3936, pp. 13–24. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Amati, G., van Rijsbergen, C.J.: Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Trans. Inf. 20(4), 357–389 (2002)CrossRefGoogle Scholar
  5. 5.
    Brin, S.: The Anatomy of a Large-Scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems, 107–117 (1998)Google Scholar
  6. 6.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5), 604–632 (1999)CrossRefGoogle Scholar
  7. 7.
    Ounis, I., Amati, G., Plachouras, V., He, B., Macdonald, C., Lioma, C.: Terrier - A High Performance and Scalable Information Retrieval Platform. In: ACM SIGIR 2006 Workshop on Open Source Information Retrieval (OSIR 2006), Seattle, Washington, USA (2006)Google Scholar
  8. 8.
    Stemmer Snowball,
  9. 9.
    The JUNG Web site,
  10. 10.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, Reading (1999)Google Scholar
  11. 11.
    Voorhees, E.M.: The Philosophy of Information Retrieval Evaluation. In: Peters, C., Braschler, M., Gonzalo, J., Kluck, M. (eds.) CLEF 2001. LNCS, vol. 2406, pp. 355–370. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    van Rijsbergen, C.J.: Information Retrieval. Butterworth (1979)Google Scholar
  13. 13.
    Leggi dltalia Professionale,
  14. 14.
    Dejure - Giuffr,
  15. 15.
  16. 16.
  17. 17.
  18. 18.
    Benjamins, R., Casanovas, P., Gangemi, A., Breuker, J.: Law and the Semantic Web - Legal Ontologies, Methodologies, Legal Information Retrieval, and Applications. Springer, Heidelberg (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Marco Bianchi
    • 1
  • Mauro Draoli
    • 1
  • Giorgio Gambosi
    • 3
    • 4
  • Giovanni Stilo
    • 2
    • 4
  1. 1.Italian National Centre for ICT in the Public Administrations (CNIPA)RomeItaly
  2. 2.Dept. of Computer ScienceUniversity of L’AquilaL’AquilaItaly
  3. 3.Dept. of MathematicsUniversity of Rome “Tor Vergata”RomeItaly
  4. 4.NESTOR - LaboratoryUniversity of Rome “Tor Vergata”RomeItaly

Personalised recommendations