Using CNL Techniques and Pattern Sentences to Involve Domain Experts in Modeling

  • Silvie Spreeuwenberg
  • Jeroen van Grondelle
  • Ronald Heller
  • Gartjan Grijzen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7175)


Involving domain experts in modeling is important when knowledge needs to be captured in a model and only domain experts can establish whether the models are correct. We have experienced that a natural language based representation of a model helps them to understand the semantics of a model and has advantages over a visual representation. Therefore a controlled natural language (CNL) is designed for our existing semantic reasoning tool Be Informed, which is based on conceptual graphs. The resulting CNL has a formal logical basis but the goal of the CNL representation is to improve readability for human readers. We report on the challenge to develop a CNL that 1) is easy and intuitively readable for domain experts with no background in formal logics, 2) can be easily generated from the formal representation and 3) can be easily adjusted for other natural languages and cultural preferences. The solution uses patterns to represent the CNL that map to the conceptual graph. The patterns are based on SBVR’s RuleSpeak and can be easily adjusted for local differences.


Controlled Natural Language Business Rules Specifications Knowledge Representation CNL Design and Evaluation SBVR RuleSpeak 


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  1. 1.
    Angelov, K., Ranta, A.: Implementing Controlled Languages in GF. In: Fuchs, N.E. (ed.) CNL 2009. LNCS (LNAI), vol. 5972, pp. 82–101. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Clark, P., Murray, W.R., Harrison, P., Thompson, J.: Naturalness vs. Predictability: A Key Debate in Controlled Languages. In: Fuchs, N.E. (ed.) CNL 2009. LNCS, vol. 5972, pp. 65–81. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Eclipse Modeling Project,
  4. 4.
    Funk, A., Tablan, V., Bontcheva, K., Cunningham, H., Davis, B., Handschuh, S.: CLOnE: Controlled Language for Ontology Editing. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 142–155. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    van Grondelle, J., Heller, R., van Haandel, E., Verburg, T.: Involving Business Users in Formal Modeling Using Natural Language Pattern Sentences. In: Cimiano, P., Pinto, H.S. (eds.) EKAW 2010. LNCS, vol. 6317, pp. 31–43. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Grönniger, H., Krahn, H., Rumpe, B., Schindler, M., Völkel, S.: Text-based Modeling. In: Proceedings of the 4th International Workshop on Software Language Engineering (2007)Google Scholar
  7. 7.
    Halpin, T.: Business Rule Verbalization. In: Proceedings of ISTA (2004)Google Scholar
  8. 8.
    Halpin, T., Curland, M.: Automated Verbalization for ORM 2. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2006 Workshops, Part II. LNCS, vol. 4278, pp. 1181–1190. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Heller, R., van Teeseling, F., Gülpers, M.: A Knowledge Infrastructure for the Dutch Immigration Office. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part II. LNCS, vol. 6089, pp. 386–390. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Jarrar, M., Keet, M., Dongilli, P.: Multilingual verbalization of ORM conceptual models and axiomatized ontologies. Technical report. STARLab, Vrije Universiteit Brussel (2006)Google Scholar
  11. 11.
    Kaljurand, K., Fuchs, N.E.: Verbalizing OWL in Attempto Controlled English. In: Proceedings of Third International Workshop on OWL: Experiences and Directions, Innsbruck, Austria (2007)Google Scholar
  12. 12.
    Kuhn, T.: AceWiki: Collaborative Ontology Management in Controlled Natural Language. In: Proceedings of the 3rd Semantic Wiki Workshop. CEUR Workshop Proceedings (2008)Google Scholar
  13. 13.
    Molto Project: Multilingual On-Line Translation,
  14. 14.
    Monnet Project: Multilingual Ontologies for Networked Knowledge,
  15. 15.
    Object Management Group: Semantics of Business Vocabulary and Rules (2008),
  16. 16.
    Power, R., Scott, D., Evans, R.: What you see is what you meant: direct knowledge editing with natural language feedback. In: Proceedings of the 13th Biennial European Conference on Artificial Intelligence, Brighton, UK, pp. 675-681 (1998)Google Scholar
  17. 17.
    Reiter, E.: NLG vs. Templates. In: Proceedings of the 5th European Workshop on Natural Language Generation, Leiden, The Netherlands, pp. 95–105 (1995)Google Scholar
  18. 18.
    Ross, R.G.: RuleSpeak (2009),

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Silvie Spreeuwenberg
    • 1
  • Jeroen van Grondelle
    • 2
  • Ronald Heller
    • 2
  • Gartjan Grijzen
    • 2
  1. 1.LibRTAmsterdamThe Netherlands
  2. 2.Be InformedApeldoornThe Netherlands

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