Article

Generating natural language texts from business process models

Authors:

Henrik Leopold,

Artem PolyvyanyyAuthors Info & Claims

CAiSE'12: Proceedings of the 24th international conference on Advanced Information Systems Engineering

Pages 64 - 79

https://doi.org/10.1007/978-3-642-31095-9_5

Published: 25 June 2012 Publication History

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.

References

[1]

Rosemann, M.: Potential Pitfalls of Process Modeling: Part A. Business Process Management Journal 12(2), 249-254 (2006)

[2]

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)

[3]

Nijssen, G., Halpin, T.: Conceptual Schema and Relational Database Design: a fact oriented approach. Prentice-Hall, Inc. (1989)

Digital Library

[4]

Frederiks, P., Weide, T.: Information modeling: The process and the required competencies of its participants. Data & Knowledge Engineering 58(1), 4-20 (2006)

Digital Library

[5]

OMG: Business process model and notation (bpmn) version 2.0. (2011)

[6]

Reiter, E.: Nlg vs. templates. In: Proceedings of the Fifth European Workshop on Natural Language Generation, pp. 95-106 (1995)

[7]

Reiter, E., Dale, R.: Building applied natural language generation systems. Nat. Lang. Eng. 3, 57-87 (1997)

Digital Library

[8]

Cahill, l., et al.: In search of a reference architecture for nlg systems, 77-85 (1999)

[9]

van Deemter, K., Krahmer, E., Theune, M.: Real versus Template-Based Natural Language Generation: A False Opposition? Comput. Linguistics 31(1), 15-24 (2005)

Digital Library

[10]

Reiter, E., Mellish, C.: Optimizing the costs and benefits of natural language generation. In: IJCAI, pp. 1164-1171 (1993)

[11]

Galley, M., Fosler-Lussier, E., Potamianos, A.: Hybrid natural language generation for spoken dialogue systems (2001)

[12]

Reiter, E., Mellish, C., Levine, J., Bridge, S.: Automatic generation of on-line documentation in the idas project. In: ANLP, pp. 64-71 (1992)

Digital Library

[13]

Leopold, H., Smirnov, S., Mendling, J.: Recognising Activity Labeling Styles in Business Process Models. EMISA 6(1), 16-29 (2011)

[14]

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)

Digital Library

[15]

Vanhatalo, J., Völzer, H., Koehler, J.: The refined process structure tree. Data & Knowledge Engineering 68(9), 793-818 (2009)

Digital Library

[16]

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)

Digital Library

[17]

Meteer, M. W.: Expressibility and the Problem of Efficient Text Planning. St. Martin's Press, Inc., New York (1992)

Digital Library

[18]

Stede, M.: Lexicalization in natural language generation: A survey. Artificial Intelligence Review 8, 309-336 (1994)

[19]

Dalianis, H.: Aggregation in natural language generation. Computational Intelligence 15(4), 384-414 (1999)

[20]

Kibble, R., Power, R.: An integrated framework for text planning and pronominalisation. In: Natural Language Generation, pp. 77-84. ACL (2000)

Digital Library

[21]

Lavoie, B., Rambow, O.: A fast and portable realizer for text generation systems. In: Applied Natural Language Processing, pp. 265-268. ACL (1997)

Digital Library

[22]

Busemann, S.: Best-first surface realization. Interface 10 (1996)

[23]

Goldberg, E., Driedger, N., Kittredge, R.: Using natural-language processing to produce weather forecasts. IEEE Expert 9(2), 45-53 (1994)

Digital Library

[24]

Dixon, R.: Deriving Verbs in English. Language Sciences 30(1), 31-52 (2008)

[25]

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)

Digital Library

[26]

Klein, D., Manning, C. D.: Fast Exact Inference with a Factored Model for Natural Language Parsing. In: NIPS 2003, vol. 15. MIT Press (2003)

[27]

Miller, G.: WordNet: a lexical database for English. Comm. ACM 38(11), 39-41 (1995)

Digital Library

[28]

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)

Digital Library

[29]

zurMuehlen, 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)

Digital Library

[30]

Polyvyanyy, A., García-Bañuelos, L., Dumas, M.: Structuring acyclic process models. Information Systems (to appear, 2012)

Digital Library

[31]

Flesch, R.: How to test readability (1951)

[32]

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)

[33]

Lavoie, B., Rambow, O., Reiter, E.: The modelexplainer. In: Proceedings of the 8th International Workshop on Natural Language Generation, pp. 9-12 (1996)

[34]

Meziane, F., Athanasakis, N., Ananiadou, S.: Generating natural language specifications from uml class diagrams. Requirements Engineering 13, 1-18 (2008)

Digital Library

[35]

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)

[36]

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)

[37]

Whitley, K. N.: Visual programming languages and the empirical evidence for and against. J. Vis. Lang. Comput. 8(1), 109-142 (1997)

[38]

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)

Digital Library

[39]

Mayer, R.: Multimedia Learning, 2nd edn. Cambridge Univ. Press (2009)

Digital Library

[40]

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)

Digital Library

[41]

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)

Digital Library

[42]

OMG: Semantics of Business Vocabulary and Business Rules (SBVR) (2008)

Cited By

Zhu RHu QWen LLin LXiao HWang C(2024)LLM-Based Business Process Documentation GenerationService-Oriented Computing10.1007/978-981-96-0805-8_27(381-390)Online publication date: 4-Dec-2024
https://dl.acm.org/doi/10.1007/978-981-96-0805-8_27
Silva Rosa LSoares Silva TFantinato MHeloisa Thom L(2022)A visual approach for identification and annotation of business process elements in process descriptionsComputer Standards & Interfaces10.1016/j.csi.2021.10360181:COnline publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1016/j.csi.2021.103601
Fontenla-Seco YLama MBugarín A(2020)Process-To-Text: A Framework for the Quantitative Description of Processes in Natural LanguageTrustworthy AI - Integrating Learning, Optimization and Reasoning10.1007/978-3-030-73959-1_19(212-219)Online publication date: 4-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-73959-1_19
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Information & Contributors

Information

Published In

cover image Guide Proceedings

CAiSE'12: Proceedings of the 24th international conference on Advanced Information Systems Engineering

June 2012

702 pages

ISBN:9783642310942

Editors:
Jolita Ralyté
Institute of Services Science, University of Geneva, Route de Drize 7, Carouge, Switzerland
,
Xavier Franch
Department ESSI - UPC, Technical University of Catalonia, c/Jordi Girona 1-3,, Barcelona, Spain
,
Sjaak Brinkkemper
Department of Information and Computing Sciences, Utrecht University, c/Jordi Girona 1-3,, Utrecht, The Netherlands
,
Stanislaw Wrycza
Department of Business Informatics, Faculty of Management, University of Gdansk, ul. Piaskowa 9, Sopot, Poland

Sponsors

Univ. of Gdańsk: University of Gdańsk

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 25 June 2012

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Cited By

Zhu RHu QWen LLin LXiao HWang C(2024)LLM-Based Business Process Documentation GenerationService-Oriented Computing10.1007/978-981-96-0805-8_27(381-390)Online publication date: 4-Dec-2024
https://dl.acm.org/doi/10.1007/978-981-96-0805-8_27
Silva Rosa LSoares Silva TFantinato MHeloisa Thom L(2022)A visual approach for identification and annotation of business process elements in process descriptionsComputer Standards & Interfaces10.1016/j.csi.2021.10360181:COnline publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1016/j.csi.2021.103601
Fontenla-Seco YLama MBugarín A(2020)Process-To-Text: A Framework for the Quantitative Description of Processes in Natural LanguageTrustworthy AI - Integrating Learning, Optimization and Reasoning10.1007/978-3-030-73959-1_19(212-219)Online publication date: 4-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-73959-1_19
Bazhenova EZerbato FOliboni BWeske M(2019)From BPMN process models to DMN decision modelsInformation Systems10.1016/j.is.2019.02.00183:C(69-88)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1016/j.is.2019.02.001
Nikaj AWeske MMendling J(2019)Semi-automatic derivation of RESTful choreographies from business process choreographiesSoftware and Systems Modeling (SoSyM)10.1007/s10270-017-0653-218:2(1195-1208)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s10270-017-0653-2
de Almeida Bordignon AThom LSilva TDani VFantinato MFerreira R(2018)Natural Language Processing in Business Process Identification and ModelingProceedings of the XIV Brazilian Symposium on Information Systems10.1145/3229345.3229373(1-8)Online publication date: 4-Jun-2018
https://dl.acm.org/doi/10.1145/3229345.3229373
Silva TThom LWeber Ade Oliveira JFantinato M(2018)Empirical Analysis of Sentence Templates and Ambiguity Issues for Business Process DescriptionsOn the Move to Meaningful Internet Systems. OTM 2018 Conferences10.1007/978-3-030-02610-3_16(279-297)Online publication date: 22-Oct-2018
https://dl.acm.org/doi/10.1007/978-3-030-02610-3_16
Figl KRecker J(2016)Process innovation as creative problem solvingInformation and Management10.1016/j.im.2016.02.00853:6(767-786)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1016/j.im.2016.02.008
Michael JAl Machot FMayr HMakedon FMariottini GKorn OMaglogiannis IMetsis V(2015)ADOxx based tool support for a behavior centered modeling approachProceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments10.1145/2769493.2769503(1-8)Online publication date: 1-Jul-2015
https://dl.acm.org/doi/10.1145/2769493.2769503

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