From Regulatory Texts to BRMS: How to Guide the Acquisition of Business Rules?

  • Abdooulaye Guissé
  • François Lévy
  • Adeline Nazarenko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7438)


This paper tackles the problem of rule acquisition, which is critical for the development of BRMS. The proposed approach assumes that regulations written in natural language (NL) are an important source of knowledge but that turning them into formal statements is a complex task that cannot be fully automated. The present paper focuses on the first phase of this acquisition process, the normalization phase that aims at transforming NL statements into controlled language (CL), rather than on their formalization into an operational rule base. We show that turning a NL text into a set of self-sufficient and independent CL rules is itself a complex task that involves some lexical and syntactic normalizations but also the restoration of contextual information and of implicit semantic entities to get a set of self-sufficient and unambiguous rule statements. We also present the SemEx tool that supports the proposed acquisition methodology based on the selection of the relevant text fragments and their progressive and interactive transformation into CL rule statements.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bajec, M., Krisper, M.: Issues and challenges in business rule-based information systems development. In: ECIS (2005)Google Scholar
  2. 2.
    Bajwa, I.S., Lee, M.G., Bordbar, B.: Sbvr business rules generation from natural language specification. In: AAAI Spring Symposium 2011 Artificial Intelligence 4 Business Agility, pp. 541–545. AAAI Press, San Francisco (2011)Google Scholar
  3. 3.
    BRG: Defining business rules what are they really? The Business Rules Group : formerly, known as the GUIDE Business Rules Project - Final Report revision 1.3 (July 2000)Google Scholar
  4. 4.
    Brodie, C., Karat, C.-M., Karat, J.: An empirical study of natural language parsing of privacy policy rules using the sparcle policy workbench. In: SOUPS 2006 (2006)Google Scholar
  5. 5.
    Candido Jr., A., Maziero, E., Gasperin, C., Pardo, T.A.S., Specia, L., Aluisio, S.M.: Supporting the adaptation of texts for poor literacy readers: a text simplification editor for brazilian portuguese. In: Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications, EdAppsNLP 2009, pp. 34–42. Association for Computational Linguistics, Stroudsburg (2009)CrossRefGoogle Scholar
  6. 6.
    Chandrasekar, R., Doran, C., Srinivas, B.: Motivations and methods for text simplification. In: Proceedings of the Sixteenth International Conference on Computational Linguistics (COLING 1996), pp. 1041–1044 (1996)Google Scholar
  7. 7.
    Chandrasekar, R., Srinivas, B.: Automatic induction of rules for text simplification (1997)Google Scholar
  8. 8.
    Dinesh, N., Joshi, A., Lee, I., Sokolsky, O.: Reasoning about Conditions and Exceptions to Laws in Regulatory Conformance Checking. In: van der Meyden, R., van der Torre, L. (eds.) DEON 2008. LNCS (LNAI), vol. 5076, pp. 110–124. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Dubauskaite, R., Vasilecas, O.: An open issues in business rules based information system development. In: Innovative Infotechnologies for Science, Business and Education, vol. 1 (2009)Google Scholar
  10. 10.
    Gasperin, C., Specia, L., Pereira, T.F., Aluisio, S.M.: Learning when to simplify sentences for natural text simplification. In: ENIA 2009 (VII Encontro Nacional de Inteligência Artificial) (2009)Google Scholar
  11. 11.
    Halle, B., Goldberg, L., Zackman, J.: Business Rule Revolution: Running Business the Right Way. Happy About (2006),
  12. 12.
    Lévy, F., Nazarenko, A., Guissé, A., Omrane, N., Szulman, S.: An environment for the joint management of written policies and business rules. In: Proceedings of the International Conference on Tools with Artificial Intelligence (IEEE-ICTAI 2010), pp. 142–149 (2010)Google Scholar
  13. 13.
    Max, A.: Simplification interactive pour la production de textes adaptés aux personnes souffrant de troubles de la compréhension. In: Proceedings of TALN, poster session (2005)Google Scholar
  14. 14.
  15. 15.
    Omrane, N., Nazarenko, A., Rosina, P., Szulman, S., Westphal, C.: Lexicalized ontology for a business rules management platform: An automotive use case. In: Proceedings of the 5th International Symposium on Rules, International Business Rules Forum (RuleMF@BRF), Ft Lauderdale, Florida, USA (November 2011)Google Scholar
  16. 16.
    Ross, R.G.: Principles of the Business Rule Approach, ch. 8-12. Addison-Wesley, Boston (2003)Google Scholar
  17. 17.
    Siddharthan, A., Caius, G.: Syntactic simplification and text cohesion (2003)Google Scholar
  18. 18.
    Wagner, G., Lukichev, S., Fuchs, N.E., Spreeuwenberg, S.: First-version controlled english rule language. In: REWERSE IST 506779 Report I1-D2 (February 2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Abdooulaye Guissé
    • 1
  • François Lévy
    • 1
  • Adeline Nazarenko
    • 1
  1. 1.Laboratoire d’Informatique de Paris-Nord (LIPN), CNRS (UMR 7030)Université Paris 13, Sorbonne Paris CitéVilletaneuseFrance

Personalised recommendations