Parsing SBVR-Based Controlled Languages

  • Mathias Kleiner
  • Patrick Albert
  • Jean Bézivin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5795)


Conceptual schemas (CS) are core elements of information systems knowledge. A challenging issue in the management processes is to allow decision makers, such as business people, to directly define and refine their schemas using a pseudo-natural language. The recently published Semantics for Business Vocabulary and Rules (SBVR) is a good candidate for an intermediate layer: it offers an abstract syntax able to express a CS, as well as a concrete syntax based on structured English. In this article, we propose an original method for extracting a SBVR terminal model out of a controlled English text and then transform it into a UML class diagram. We describe a model-driven engineering approach in which constraint-programming based search is combined with model transformation. The use of an advanced resolution technique (configuration) as an operation on models allows for non-deterministic parsing and language flexibility. In addition to the theoretical results, preliminary experiments on a running example are provided.


Controlled languages parsing SBVR model-driven engineering constraints configuration model search 


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  1. 1.
    Jouault, F., Kurtev, I.: Transforming Models with ATL. In: Bruel, J.-M. (ed.) MoDELS 2005. LNCS, vol. 3844, pp. 128–138. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Object Management Group: Meta Object Facility (MOF) 2.0 Query/View/Transformation (QVT) Specification, version 1.0 (2008)Google Scholar
  3. 3.
    Semantics of Business Vocabulary and Business Rules (SBVR) 1.0 specification (2008),
  4. 4.
    Schwitter, R., Fuchs, N.E.: Attempto controlled english (ace) a seemingly informal bridgehead in formal territory. In: JICSLP, p. 536 (1996)Google Scholar
  5. 5.
    Kaljurand, K.: Ace view - an ontology and rule editor based on controlled english. In: International Semantic Web Conference (Posters & Demos). CEUR Workshop Proceedings, vol. 401, (2008)Google Scholar
  6. 6.
  7. 7.
  8. 8.
    Junker, U.: 26. In: Configuration. Volume Handbook of Constraint Programming. Elsevier, Amsterdam (2006)Google Scholar
  9. 9.
    Mittal, S., Falkenhainer, B.: Dynamic constraint satisfaction problems. In: Proceedings of AAAI 1990, pp. 25–32 (1990)Google Scholar
  10. 10.
    Stumptner, M., Haselböck, A.: A generative constraint formalism for configuration problems. In: Advances in Artificial Intelligence: Proceedings of AI*IA 1993, pp. 302–313. Springer, Heidelberg (1993)CrossRefGoogle Scholar
  11. 11.
    Sabin, D., Freuder, E.C.: Composite constraint satisfaction. In: AI and Manufacturing Research Planning Workshop, pp. 153–161 (1996)Google Scholar
  12. 12.
    Stumptner, M.: An overview of knowledge-based configuration. AI Communications 10(2), 111–125 (1997)Google Scholar
  13. 13.
    Soininen, T., Niemela, I., Tiihonen, J., Sulonen, R.: Representing configuration knowledge with weight constraint rules. In: Proceedings of the AAAI Spring Symp. on Answer Set Programming, pp. 195–201 (2001)Google Scholar
  14. 14.
    Mailharro, D.: A classification and constraint-based framework for configuration. AI in Engineering, Design and Manufacturing (12), 383–397 (1998)Google Scholar
  15. 15.
    Junker, U., Mailharro, D.: The logic of (j)configurator: Combining constraint programming with a description logic. In: IJCAI 2003. Springer, Heidelberg (2003)Google Scholar
  16. 16.
    Estratat, M., Henocque, L.: Parsing languages with a configurator. In: Proceedings of the European Conference for Artificial Intelligence ECAI 2004, August 2004, pp. 591–595 (2004)Google Scholar
  17. 17.
    Felfernig, A., Friedrich, G., Jannach, D., Zanker, M.: Configuration knowledge representation using uml/ocl. In: Jézéquel, J.-M., Hussmann, H., Cook, S. (eds.) UML 2002. LNCS, vol. 2460, pp. 49–62. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  18. 18.
    Jouault, F., Bézivin, J.: Km3: A dsl for metamodel specification. In: Gorrieri, R., Wehrheim, H. (eds.) FMOODS 2006. LNCS, vol. 4037, pp. 171–185. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  19. 19.
    Kittredge, R.I.: Sublanguages and controlled languages. Oxford Press (2003)Google Scholar
  20. 20.
    Blache, P., Balfourier, J.-M.: Property grammars: a flexible constraint-based approach to parsing. In: IWPT. Tsinghua University Press (2001)Google Scholar
  21. 21.
  22. 22.
    Cabot, J., Pau, R., Raventós, R.: From uml/ocl to sbvr specifications: a challenging transformation. In: Information Systems. Elsevier, Amsterdam (2009)Google Scholar
  23. 23.
    Cabot, J., Clarisó, R., Riera, D.: Umltocsp: a tool for the formal verification of uml/ocl models using constraint programming. In: ASE, pp. 547–548. ACM, New York (2007)CrossRefGoogle Scholar
  24. 24.
    Dinh-Trong, T.T., Ghosh, S., France, R.B.: A systematic approach to generate inputs to test uml design models. In: ISSRE, pp. 95–104. IEEE Computer Society, Los Alamitos (2006)Google Scholar
  25. 25.
    Jackson, D.: Automating first-order relational logic. In: SIGSOFT FSE, pp. 130–139 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mathias Kleiner
    • 1
  • Patrick Albert
    • 2
  • Jean Bézivin
    • 1
  1. 1.INRIA - EMN, Atlanmod teamNantesFrance
  2. 2.ILOG S.AGentillyFrance

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