Skip to main content
SpringerLink
Log in

Unleashing textual descriptions of business processes

  • Regular Paper
  • Published:
Software and Systems Modeling Aims and scope Submit manuscript

Abstract

Textual descriptions of processes are ubiquitous in organizations, so that documentation of the important processes can be accessible to anyone involved. Unfortunately, the value of this rich data source is hampered by the challenge of analyzing unstructured information. In this paper we propose a framework to overcome the current limitations on dealing with textual descriptions of processes. This framework considers extraction and analysis and connects to process mining via simulation. The framework is grounded in the notion of annotated textual descriptions of processes, which represents a middle-ground between formalization and accessibility, and which accounts for different modeling styles, ranging from purely imperative to purely declarative. The contributions of this paper are implemented in several tools, and case studies are highlighted.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. http://nlp.cs.upc.edu/freeling

  2. The term patient, as used in the formalization, is not related to the medical term used in the running example. Instead, it is borrowed from the related concept in the field of linguistics.

  3. We keep a pair for simplicity of presentation, but all definitions carry over to n-ary tuples of sub-blocks.

  4. It is interesting to notice that Declare itself was defined by relying on the patterns originally introduced in [17].

  5. See https://nlp.stanford.edu/software/tregex.html.

  6. The proposed query does not account for the start and end activities of scopes, which are not present in the original trace. A slightly more complex version can be crafted that accounts for any invisible activity to be present between the visible activities of the trace. We do not show it here for the sake of simplicity.

References

  1. Decision Model and Notation (DMN). Standard, OMG-Object Management Group (2016)

  2. Arora, C., Sabetzadeh, M., Briand, L., Zimmer, F.: Automated checking of conformance to requirements templates using natural language processing. IEEE Trans. Softw. Eng. 41(10), 944–968 (2015)

    Article  Google Scholar 

  3. Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: A genetic algorithm for discovering process trees. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2012)

  4. Burattin, A.: PLG2: multiperspective process randomization with online and offline simulations. In: BPM Demo Track (2016)

  5. Carmona, J., van Dongen, B.F., Solti, A., Weidlich, M.: Conformance Checking: Relating Processes and Models. Springer, Berlin (2018)

    Book  Google Scholar 

  6. Chen, Y., Xu, L., Liu, K., Zeng, D., Zhao, J.: Event extraction via dynamic multi-pooling convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 167–176, Beijing, China. Association for Computational Linguistics (2015)

  7. Di Ciccio, C., Bernardi, M.L., Cimitile, M., Maggi, F.M.: Generating event logs through the simulation of declare models. EOMAS 2015, 20–36 (2015)

    Google Scholar 

  8. Cimatti, Alessandro, Clarke, Edmund M., Giunchiglia, Fausto, Roveri, Marco: NUSMV: a new symbolic model checker. STTT 2(4), 410–425 (2000)

    Article  Google Scholar 

  9. Claes, J., Vanderfeesten, I., Pinggera, J., Reijers, H.A., Weber, B., Poels, G.: A visual analysis of the process of process modeling. Inf. Syst. e-Bus. Manag. 13(1), 147–190 (2015)

    Article  Google Scholar 

  10. Das, R., Munkhdalai, T., Yuan, X., Trischler, A., McCallum, A.: Building dynamic knowledge graphs from text using machine reading comprehension. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6–9, 2019. OpenReview.net (2019)

  11. De Giacomo, G., De Masellis, R., Montali, M.: Reasoning on LTL on finite traces: insensitivity to infiniteness. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp. 1027–1033. AAAI Press (2014)

  12. de Medeiros, A.K.A., Günther, C.W.: Process mining: using CPN tools to create test logs for mining algorithms. In: Proceedings of the Sixth Workshop and Tutorial on Practical Use of Coloured Petri Nets and the CPN Tools, pp. 177–190 (2005)

  13. Delicado, L., Sanchez-Ferreres, J., Carmona, J., Padró, L.: NLP4BPM: natural language processing tools for business process management. In: BPM Demo Track (2017)

  14. Delicado, L., Sanchez-Ferreres, J., Carmona, J., Pardo, L.: The model judge: a tool for supporting novices in learning process modeling. In: BPM Demo Track (2018)

  15. Dijkman, Remco, Vanderfeesten, Irene, Reijers, Hajo A.: Business process architectures: overview, comparison and framework. Enterp. Inf. Syst. 10(2), 129–158 (2016)

    Article  Google Scholar 

  16. Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer, Berlin (2018)

    Book  Google Scholar 

  17. Dwyer, M.B., Avrunin, G.S., Corbett, J.C.: Patterns in property specifications for finite-state verification. In: Proceedings of ICSE, pp. 411–420. ACM (1999)

  18. Fellbaum, Christiane: WordNet. An Electronic Lexical Database. Language, Speech, and Communication. The MIT Press (1998)

  19. Feng, W., Zhuo, H.H., Kambhampati, S.: Extracting action sequences from texts based on deep reinforcement learning. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pp. 4064–4070. International Joint Conferences on Artificial Intelligence Organization, 7 (2018)

  20. Günther, F., Christian, K., Mayr, H.C.: From textual scenarios to a conceptual schema. Data Knowl. Eng. 55(1), 20–37 (2005)

    Article  Google Scholar 

  21. Friedrich, F., Mendling, J., Puhlmann, F.: Process model generation from natural language text. In: Mouratidis, H., Rolland, C. (eds.) Advanced Information Systems Engineering, pp. 482–496. Springer, Berlin (2011)

    Google Scholar 

  22. De Giacomo, G., Vardi, M.Y.: Linear temporal logic and linear dynamic logic on finite traces. In: IJCAI (2013)

  23. Halioui, A., Valtchev, P., Diallo, A.B.: Bioinformatic workflow extraction from scientific texts based on word sense disambiguation. IEEE/ACM Trans. Comput. Biol. Bioinform. 15(6), 1979–1990 (2018)

    Article  Google Scholar 

  24. Jouck, T.: Empirically evaluating process mining algorithms: towards closing the methodological gap. Ph.D. thesis, University of Hasselt (2018)

  25. Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs: a constructive approach. In: Proceedings of PETRI NETS, pp. 311–329. Springer (2013)

  26. Leopold, H.: Natural language in business process models. Ph.D. thesis, Springer (2013)

  27. Leopold, H., van der Aa, H., Reijers, H.A.: Identifying candidate tasks for robotic process automation in textual process descriptions. In: Gulden, J., Reinhartz-Berger, I., Schmidt, R., Guerreiro, S., Guédria, W., Bera, P. (eds.) Enterprise, Business-Process and Information Systems Modeling: 19th International Conference, BPMDS 2018, 23rd International Conference, EMMSAD 2018, Held at CAiSE 2018, Tallinn, Estonia, June 11–12, 2018, Proceedings, volume 318 of Lecture Notes in Business Information Processing, pp. 67–81. Springer (2018)

  28. Levy, R., Andrew, G.: Tregex and tsurgeon: tools for querying and manipulating tree data structures. In: LREC, pp. 2231–2234. Citeseer (2006)

  29. Maqbool, B., Azam, F., Anwar, M.W., Butt, W.H., Zeb, J., Zafar, I., Nazir, A.K., Umair, Z.: A comprehensive investigation of BPMN models generation from textual requirements: techniques, tools and trends. In: Information Science and Applications 2018: ICISA 2018, Hong Kong, China, June 25–27th, 2018, volume 514 of Lecture Notes in Electrical Engineering, pp. 543–557. Springer (2019)

  30. Mendling, J., Baesens, B., Bernstein, A., Fellmann, M.: Challenges of smart business process management: an introduction to the special issue. DSS 100, 1–5 (2017)

    Google Scholar 

  31. Mendling, J., Leopold, H., Pittke, F.: 25 Challenges of semantic process modeling. Int. J. Inf. Syst. Softw. Eng. Big Co. 1(1), 78–94 (2015)

    Google Scholar 

  32. Nunes, V.T., Santoro, F.M., Borges, M.R.S.: A context-based model for knowledge management embodied in work processes. Inf. Sci. 179(15), 2538–2554 (2009)

    Article  Google Scholar 

  33. Ottensooser, Avner, Fekete, Alan, Reijers, Hajo A., Mendling, Jan, Menictas, Con: Making sense of business process descriptions: an experimental comparison of graphical and textual notations. J. Syst. Softw. 85(3), 596–606 (2012)

    Article  Google Scholar 

  34. Padró, L., Stanilovsky, E.: Freeling 3.0: towards wider multilinguality. In: Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC), pp. 2473–2479 (2012)

  35. Pesic, M., Schonenberg, H., van der Aalst, W.M.P.: DECLARE: full support for loosely-structured processes. In: Proceedings of the IEEE International Enterprise Distributed Object Computing Conference, pp. 287–298. IEEE Computer Society (2007)

  36. Pinggera, J.: The process of process modeling. Ph.D. thesis, University of Innsbruck, Department of Computer Science (2014)

  37. Pnueli, A.: The temporal logic of programs, pp. 46–57. IEEE (1977)

  38. Qian, C., Wen, L., Kumar, A., Lin, L., Lin, L., Zong, Z., Li, S., Wang, J.: An approach for process model extraction by multi-grained text classification. In: Dustdar, S., Yu, E., Salinesi, C., Rieu, D., Pant, V. (eds.) Advanced Information Systems Engineering, pp. 268–282. Springer, Cham (2020)

    Chapter  Google Scholar 

  39. Rozier, K.Y., Vardi, M.Y.: LTL satisfiability checking

  40. Sànchez-Ferreres, J., Burattin, A., Carmona, J., Montali, M., Padró, L.: Formal reasoning on natural language descriptions of processes. In: Proceedings of BPM, pp. 86–101 (2019)

  41. Sànchez-Ferreres, J., Carmona, J., Padró, L.: Aligning textual and graphical descriptions of processes through ILP techniques. In: Proceedings of CAiSE, pp. 413–427 (2017)

  42. Sànchez-Ferreres, J., Delicado, L., Andaloussi, A.A., Burattin, A., Calderón-Ruiz, G., Weber, B., Carmona, J., Padró, L.: Supporting the process of learning and teaching process models. IEEE Trans. Learn. Technol. 13(3), 552–566 (2020)

    Article  Google Scholar 

  43. Sànchez-Ferreres, J., van der Aa, H., Carmona, J., Padró, L.: Aligning textual and model-based process descriptions. Data Knowl. Eng. 118, 25–40 (2018)

    Article  Google Scholar 

  44. Schumacher, P.: Workflow extraction from textual process descriptions. Ph.D. thesis, Johann Wolfgang Goethe Universität Frankfurt/Main (2016)

  45. Semmelrodt, F.: Modellierung klinischer Prozesse und Compliance Regeln mittels BPMN 2.0 und eCRG. Ph.D. thesis, University of Ulm (2013)

  46. Di Sorbo, A., Panichella, S., Visaggio, C.A., Di Penta, M., Canfora, G., Gall, H.C.: Exploiting natural language structures in software informal documentation. IEEE Trans. Softw. Eng. (2019)

  47. Stenetorp, P., Pyysalo, S., Topić, G., Ohta, T., Ananiadou, S., Tsujii, J.: Brat: a web-based tool for NLP-assisted text annotation. In: Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics, pp. 102–107. Association for Computational Linguistics (2012)

  48. Tandon, N., Dalvi, B., Grus, J., Yih, W., Bosselut, A., Clark, P.: Reasoning about actions and state changes by injecting commonsense knowledge. In: Riloff, E., Chiang, D., Hockenmaier, J., Tsujii, J. (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31–November 4, 2018, pp. 57–66. Association for Computational Linguistics (2018)

  49. van der Aa, H., Carmona, J., Leopold, H., Mendling, J., Padró, L.: Challenges and opportunities of applying natural language processing in business process management. In: Proceedings of the 27th International Conference on Computational Linguistics (COLING), pp. 2791–2801 (2018)

  50. van der Aa, H., Leopold, H., Reijers, H.A.: Detecting inconsistencies between process models and textual descriptions. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) Business Process Management—13th International Conference, BPM 2015, Innsbruck, Austria, August 31–September 3, 2015, Proceedings, volume 9253 of Lecture Notes in Computer Science, pp. 90–105. Springer (2015)

  51. van der Aa, H., Leopold, H., Reijers, H.A.: Dealing with behavioral ambiguity in textual process descriptions. In: La Rosa, M., Loos, P., Pastor, O. (eds.) Business Process Management: 14th International Conference, BPM 2016, Rio de Janeiro, Brazil, September 18–22, 2016. Proceedings, volume 9850 of Lecture Notes in Computer Science, pp. 271–288. Springer (2016)

  52. van der Aa, H., Leopold, H., Reijers, H.A.: Comparing textual descriptions to process models: the automatic detection of inconsistencies. Inf. Syst. 64, 447–460 (2017)

    Article  Google Scholar 

  53. van der Aalst, W.M.P.: Process Mining. Springer, Berlin (2016)

    Book  Google Scholar 

  54. Verbeek, E., Buijs, J.C.A.M., van Dongen, B.F.,van der Aalst, W.M.P.: Prom 6: the process mining toolkit. In: Proceedings of the Business Process Management 2010 Demonstration Track, Hoboken, NJ, USA, September 14–16, 2010 (2010)

  55. Verbeek, H.M.W., Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: XES, XESame, and ProM 6. In: Information Systems Evolution, pp. 60–75. Springer, Berlin (2011)

  56. Walter, K., Minor, M., Bergmann, R.: Workflow extraction from cooking recipes. In: Process-Oriented Case-Based Reasoning Workshop, pp. 207–216 01 (2011)

  57. Yang, B., Mitchell, T.M.: Joint extraction of events and entities within a document context. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 289–299, San Diego, California (June 2016). Association for Computational Linguistics

Download references

Acknowledgements

This work has been supported by MINECO and FEDER funds under grant TIN2017-86727-C2-1-R.

Author information

Authors and Affiliations

  1. Universitat Politècnica de Catalunya, Barcelona, Spain

    Josep Sànchez-Ferreres, Josep Carmona, Lluís Padró & Luís Quishpi

  2. Technical University of Denmark, Copenhagen, Denmark

    Andrea Burattin

  3. Free University of Bozen-Bolzano, Bolzano, Italy

    Marco Montali

Authors
  1. Josep Sànchez-Ferreres
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrea Burattin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Josep Carmona
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marco Montali
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lluís Padró
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Luís Quishpi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Josep Sànchez-Ferreres.

Additional information

Communicated by Jan Mendling.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sànchez-Ferreres, J., Burattin, A., Carmona, J. et al. Unleashing textual descriptions of business processes. Softw Syst Model 20, 2131–2153 (2021). https://doi.org/10.1007/s10270-021-00886-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10270-021-00886-x

Keywords

Search

Navigation