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Text2Dec: Extracting Decision Dependencies from Natural Language Text for Automated DMN Decision Modelling

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Business Process Management Workshops (BPM 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 397))

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Abstract

Decisions are of significant value to organisations. Business decisions are often written down in textual documents, and modelling them is a tedious and time-consuming task. Although decision modelling has seen a surge of interest since the introduction of the Decision Model and Notation (DMN) standard, limited research has been conducted regarding automatically extracting decision models from the text. In this paper, we propose a text mining technique to automatically extract the decisions and their dependencies from natural language text to build the decision requirements diagram. A case-based evaluation is shown for the proposed mining approach with promising results. This approach can serve as a groundwork for further research in the field of decision automation.

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Notes

  1. 1.

    Taken from https://www.nhlbi.nih.gov/files/docs/guidelines/prctgd_c.pdf.

  2. 2.

    https://nlp.stanford.edu/software/.

  3. 3.

    https://www.nltk.org/.

  4. 4.

    https://github.com/huggingface/neuralcoref.

  5. 5.

    https://spacy.io/usage/.

  6. 6.

    https://universaldependencies.org/u/pos/.

  7. 7.

    https://camunda.com/dmn/.

References

  1. van der Aa, H., Di Ciccio, C., Leopold, H., Reijers, H.A.: Extracting declarative process models from natural language. In: Giorgini, P., Weber, B. (eds.) CAiSE 2019. LNCS, vol. 11483, pp. 365–382. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21290-2_23

    Chapter  Google Scholar 

  2. van der Aa, H., Leopold, H., Batoulis, K., Weske, M., Reijers, H.A.: Integrated process and decision modeling for data-driven processes. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 405–417. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42887-1_33

    Chapter  Google Scholar 

  3. Batoulis, K., Meyer, A., Bazhenova, E., Decker, G., Weske, M.: Extracting decision logic from process models. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.) CAiSE 2015. LNCS, vol. 9097, pp. 349–366. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19069-3_22

    Chapter  Google Scholar 

  4. Bazhenova, E., Buelow, S., Weske, M.: Discovering decision models from event logs. In: Abramowicz, W., Alt, R., Franczyk, B. (eds.) BIS 2016. LNBIP, vol. 255, pp. 237–251. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39426-8_19

    Chapter  Google Scholar 

  5. Calvanese, D., Dumas, M., Laurson, Ü., Maggi, F.M., Montali, M., Teinemaa, I.: Semantics and analysis of DMN decision tables. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 217–233. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45348-4_13

    Chapter  Google Scholar 

  6. Campos, J., Richetti, P., Baião, F.A., Santoro, F.M.: Discovering business rules in knowledge-intensive processes through decision mining: an experimental study. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 556–567. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74030-0_44

    Chapter  Google Scholar 

  7. Danenas, P., Skersys, T., Butleris, R.: Natural language processing-enhanced extraction of SBVR business vocabularies and business rules from UML use case diagrams. Data Knowl. Eng. 19 (2020)

    Google Scholar 

  8. Dasseville, I., Janssens, L., Janssens, G., Vanthienen, J., Denecker, M.: Combining DMN and the knowledge base paradigm for flexible decision enactment. Supplementary Proceedings of the RuleML 2016 Challenge 1620 (2016)

    Google Scholar 

  9. De Smedt, J., Hasić, F., vanden Broucke, S.K., Vanthienen, J.: Holistic discovery of decision models from process execution data. Knowl.-Based Syst. 183, 15 (2019)

    Google Scholar 

  10. Dragoni, M., Villata, S., Rizzi, W., Governatori, G.: Combining NLP approaches for rule extraction from legal documents. In: MIREL (2016)

    Google Scholar 

  11. Figl, K., Mendling, J., Tokdemir, G., Vanthienen, J.: What we know and what we do not know about DMN. Enterp. Model. Inf. Syst. Archit. Int. J. Concept. Model. 13(2), 1–16 (2018)

    Google Scholar 

  12. 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). https://doi.org/10.1007/978-3-642-21640-4_36

    Chapter  Google Scholar 

  13. Froelich, J., Ananyan, S.: Decision support via text mining. In: Handbook on Decision Support Systems (2008)

    Google Scholar 

  14. Hasic, F., Vanthienen, J.: Complexity metrics for DMN decision models. Comput. Stand. Interfaces 65, 15–37 (2019)

    Article  Google Scholar 

  15. Honkisz, K., Kluza, K., Wisniewski, P.: A concept for generating business process models from natural language description. In: KSEM (2018)

    Google Scholar 

  16. Janssens, L., Bazhenova, E., Smedt, J.D., Vanthienen, J., Denecker, M.: Consistent integration of decision (DMN) and process (BPMN) models. In: CAiSE Forum. CEUR Workshop Proceedings, vol. 1612, pp. 121–128. CEUR-WS.org (2016)

    Google Scholar 

  17. Janssens, L., De Smedt, J., Vanthienen, J.: Modeling and enacting enterprise decisions. In: Krogstie, J., Mouratidis, H., Su, J. (eds.) CAiSE 2016. LNBIP, vol. 249, pp. 169–180. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39564-7_17

    Chapter  Google Scholar 

  18. Kluza, K., Honkisz, K.: From SBVR to BPMN and DMN models. proposal of translation from rules to process and decision models. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS (LNAI), vol. 9693, pp. 453–462. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39384-1_39

    Chapter  Google Scholar 

  19. Sànchez-Ferreres, J., Burattin, A., Carmona, J., Montali, M., Padró, L.: Formal reasoning on natural language descriptions of processes. In: BPM (2019)

    Google Scholar 

  20. Silver, B.: DMN Method and Style, 2nd Edition: A Business Pracitioner’s Guide to Decision Modeling. Cody-Cassidy Press (2018)

    Google Scholar 

  21. Sintoris, K., Vergidis, K.: Extracting business process models using natural language processing (NLP) techniques. In: 2017 IEEE 19th Conference on Business Informatics (CBI), vol. 1, pp. 135–139 (2017)

    Google Scholar 

  22. Taylor, J., Fish, A., Vanthienen, J., Vincent, P.: Emerging standards in decision modeling. In: Intelligent BPM Systems: Impact and Opportunity, pp. 133–146. BPM and Workflow Handbook series, iBPMS Expo (2013)

    Google Scholar 

  23. Valencia-Parra, Á., Parody, L., Varela-Vaca, Á.J., Caballero, I., Gómez-López, M.T.: DMN for data quality measurement and assessment. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds.) BPM 2019. LNBIP, vol. 362, pp. 362–374. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37453-2_30

    Chapter  Google Scholar 

  24. Vanthienen, J., Dries, E.: Illustration of a decision table tool for specifying and implementing knowledge based systems. Int. J. Artif. Intell. Tools 3(2), 267–288 (1994)

    Article  Google Scholar 

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Authors and Affiliations

  1. Leuven Institute for Research on Information Systems (LIRIS), KU Leuven, Leuven, Belgium

    Vedavyas Etikala, Ziboud Van Veldhoven & Jan Vanthienen

Authors
  1. Vedavyas Etikala
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  2. Ziboud Van Veldhoven
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  3. Jan Vanthienen
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Corresponding author

Correspondence to Vedavyas Etikala .

Editor information

Editors and Affiliations

  1. Universidad de Sevilla, Seville, Spain

    Adela Del Río Ortega

  2. Kühne Logistics University, Hamburg, Germany

    Henrik Leopold

  3. Universidade do Estado do Rio de Janeiro – UERJ, Rio de Janeiro, Brazil

    Flávia Maria Santoro

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Etikala, V., Van Veldhoven, Z., Vanthienen, J. (2020). Text2Dec: Extracting Decision Dependencies from Natural Language Text for Automated DMN Decision Modelling. In: Del Río Ortega, A., Leopold, H., Santoro, F.M. (eds) Business Process Management Workshops. BPM 2020. Lecture Notes in Business Information Processing, vol 397. Springer, Cham. https://doi.org/10.1007/978-3-030-66498-5_27

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  • DOI: https://doi.org/10.1007/978-3-030-66498-5_27

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  • Print ISBN: 978-3-030-66497-8

  • Online ISBN: 978-3-030-66498-5

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