Abstract
Process Modeling is a widely used concept for understanding, documenting and also redesigning the operations of organizations. The validation and usage of process models is however affected by the fact that only business analysts fully understand them in detail. This is in particular a problem because they are typically not domain experts. In this paper, we investigate in how far the concept of verbalization can be adapted from object-role modeling to process models. To this end, we define an approach which automatically transforms BPMN process models into natural language texts and combines different techniques from linguistics and graph decomposition in a flexible and accurate manner. The evaluation of the technique is based on a prototypical implementation and involves a test set of 53 BPMN process models showing that natural language texts can be generated in a reliable fashion.
Chapter PDF
Similar content being viewed by others
References
Rosemann, M.: Potential Pitfalls of Process Modeling: Part A. Business Process Management Journal 12(2), 249–254 (2006)
Verheijen, G., van Bekkum, J.: NIAM, an information analysis method. In: IFIP WG8.1 Conf. on Comparative Review of Inf. System Method, pp. 537–590 (1982)
Nijssen, G., Halpin, T.: Conceptual Schema and Relational Database Design: a fact oriented approach. Prentice-Hall, Inc. (1989)
Frederiks, P., Weide, T.: Information modeling: The process and the required competencies of its participants. Data & Knowledge Engineering 58(1), 4–20 (2006)
OMG: Business process model and notation (bpmn) version 2.0. (2011)
Reiter, E.: Nlg vs. templates. In: Proceedings of the Fifth European Workshop on Natural Language Generation, pp. 95–106 (1995)
Reiter, E., Dale, R.: Building applied natural language generation systems. Nat. Lang. Eng. 3, 57–87 (1997)
Cahill, l., et al.: In search of a reference architecture for nlg systems, 77–85 (1999)
van Deemter, K., Krahmer, E., Theune, M.: Real versus Template-Based Natural Language Generation: A False Opposition? Comput. Linguistics 31(1), 15–24 (2005)
Reiter, E., Mellish, C.: Optimizing the costs and benefits of natural language generation. In: IJCAI, pp. 1164–1171 (1993)
Galley, M., Fosler-Lussier, E., Potamianos, A.: Hybrid natural language generation for spoken dialogue systems (2001)
Reiter, E., Mellish, C., Levine, J., Bridge, S.: Automatic generation of on-line documentation in the idas project. In: ANLP, pp. 64–71 (1992)
Leopold, H., Smirnov, S., Mendling, J.: Recognising Activity Labeling Styles in Business Process Models. EMISA 6(1), 16–29 (2011)
Leopold, H., Mendling, J., Reijers, H.: On the Automatic Labeling of Process Models. In: Mouratidis, H., Rolland, C. (eds.) CAiSE 2011. LNCS, vol. 6741, pp. 512–520. Springer, Heidelberg (2011)
Vanhatalo, J., Völzer, H., Koehler, J.: The refined process structure tree. Data & Knowledge Engineering 68(9), 793–818 (2009)
Polyvyanyy, A., Vanhatalo, J., Völzer, H.: Simplified Computation and Generalization of the Refined Process Structure Tree. In: Bravetti, M. (ed.) WS-FM 2010. LNCS, vol. 6551, pp. 25–41. Springer, Heidelberg (2011)
Meteer, M.W.: Expressibility and the Problem of Efficient Text Planning. St. Martin’s Press, Inc., New York (1992)
Stede, M.: Lexicalization in natural language generation: A survey. Artificial Intelligence Review 8, 309–336 (1994)
Dalianis, H.: Aggregation in natural language generation. Computational Intelligence 15(4), 384–414 (1999)
Kibble, R., Power, R.: An integrated framework for text planning and pronominalisation. In: Natural Language Generation, pp. 77–84. ACL (2000)
Lavoie, B., Rambow, O.: A fast and portable realizer for text generation systems. In: Applied Natural Language Processing, pp. 265–268. ACL (1997)
Busemann, S.: Best-first surface realization. Interface 10 (1996)
Goldberg, E., Driedger, N., Kittredge, R.: Using natural-language processing to produce weather forecasts. IEEE Expert 9(2), 45–53 (1994)
Dixon, R.: Deriving Verbs in English. Language Sciences 30(1), 31–52 (2008)
Leopold, H., Smirnov, S., Mendling, J.: Refactoring of Process Model Activity Labels. In: Hopfe, C.J., Rezgui, Y., Métais, E., Preece, A., Li, H. (eds.) NLDB 2010. LNCS, vol. 6177, pp. 268–276. Springer, Heidelberg (2010)
Klein, D., Manning, C.D.: Fast Exact Inference with a Factored Model for Natural Language Parsing. In: NIPS 2003, vol. 15. MIT Press (2003)
Miller, G.: WordNet: a lexical database for English. Comm. ACM 38(11), 39–41 (1995)
Mel’cuk, I., Polguère, A.: A formal lexicon in the meaning-text theory (or how to do lexica with words). Computational Linguistics 13(3-4), 261–275 (1987)
zur Muehlen, M., Recker, J.: How Much Language Is Enough? Theoretical and Practical Use of the Business Process Modeling Notation. In: Bellahsène, Z., Léonard, M. (eds.) CAiSE 2008. LNCS, vol. 5074, pp. 465–479. Springer, Heidelberg (2008)
Polyvyanyy, A., García-Bañuelos, L., Dumas, M.: Structuring acyclic process models. Information Systems (to appear, 2012)
Flesch, R.: How to test readability (1951)
Iordanskaja, L., Kittredge, R., Polguére, A.: Lexical selection and paraphrase in a meaning-text generation model. In: Natural Language Generation in Artificial Intelligence and Computational Linguistics, pp. 293–312 (1991)
Lavoie, B., Rambow, O., Reiter, E.: The modelexplainer. In: Proceedings of the 8th International Workshop on Natural Language Generation, pp. 9–12 (1996)
Meziane, F., Athanasakis, N., Ananiadou, S.: Generating natural language specifications from uml class diagrams. Requirements Engineering 13, 1–18 (2008)
Dalianis, H.: A Method for Validating a Conceptual Model by Natural Language Discourse Generation. In: Loucopoulos, P. (ed.) CAiSE 1992. LNCS, vol. 593, pp. 425–444. Springer, Heidelberg (1992)
Rolland, C., Proix, C.: A Natural Language Approach for Requirements Engineering. In: Loucopoulos, P. (ed.) CAiSE 1992. LNCS, vol. 593, pp. 257–277. Springer, Heidelberg (1992)
Whitley, K.N.: Visual programming languages and the empirical evidence for and against. J. Vis. Lang. Comput. 8(1), 109–142 (1997)
Ottensooser, A., Fekete, A., Reijers, H.A., Mendling, J., Menictas, C.: Making sense of business process descriptions: An experimental comparison of graphical and textual notations. Journal of Systems and Software (to appear, 2012)
Mayer, R.: Multimedia Learning, 2nd edn. Cambridge Univ. Press (2009)
Ouyang, C., Dumas, M., van der Aalst, W., ter Hofstede, A., Mendling, J.: From business process models to process-oriented software systems. ACM Trans. Softw. Eng. Methodol. 19(1) (2009)
Friedrich, F., Mendling, J., Puhlmann, F.: Process Model Generation from Natural Language Text. In: Mouratidis, H., Rolland, C. (eds.) CAiSE 2011. LNCS, vol. 6741, pp. 482–496. Springer, Heidelberg (2011)
OMG: Semantics of Business Vocabulary and Business Rules (SBVR) (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Leopold, H., Mendling, J., Polyvyanyy, A. (2012). Generating Natural Language Texts from Business Process Models. In: Ralyté, J., Franch, X., Brinkkemper, S., Wrycza, S. (eds) Advanced Information Systems Engineering. CAiSE 2012. Lecture Notes in Computer Science, vol 7328. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31095-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-31095-9_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31094-2
Online ISBN: 978-3-642-31095-9
eBook Packages: Computer ScienceComputer Science (R0)