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Natural language techniques supporting decision modelers

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Abstract

Decision Model and Notation (DMN) has become a relevant topic for organizations since it allows users to control their processes and organizational decisions. The increasing use of DMN decision tables to capture critical business knowledge raises the need for supporting analysis tasks such as the extraction of inputs, outputs and their relations from natural language descriptions. In this paper, we create a stepping stone towards implementing a Natural Language Processing framework to model decisions based on the DMN standard. Our proposal contributes to the generation of decision rules and tables from a single sentence analysis. This framework comprises three phases: (1) discourse and semantic analysis, (2) syntactic analysis and (3) decision table construction. To the best of our knowledge, this is the first attempt devoted to automatically discovering decision rules according to the DMN terminology from natural language descriptions. Aiming at assessing the quality of the resultant decision tables, we have conducted a survey involving 16 DMN experts. The results have shown that our framework is able to generate semantically correct tables. It is convenient to mention that our proposal does not aim to replace analysts but support them in creating better models with less effort.

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Notes

  1. http://www.omg.org/spec/BPMN/2.0/.

  2. http://www.omg.org/spec/DMN/.

  3. https://www.omg.org/spec/SBVR/About-SBVR/.

  4. http://dmg.org/pmml/v4-3/GeneralStructure.html.

  5. http://www.rulespeak.com/en.

  6. https://www.signavio.com/.

  7. https://www.trisotech.com/.

  8. https://gate.ac.uk/.

  9. https://nlp.stanford.edu/

  10. https://verbs.colorado.edu/verbnet/.

  11. https://wordnet.princeton.edu/.

  12. http://groups.inf.ed.ac.uk/ccg/software.html.

  13. The Penn Treebank is a corpus of manually parsed newspaper articles.

  14. The description of each dependency tag appears in the Stanford typed dependencies manual (Marneffe and Manning 2010).

  15. https://catalog.ldc.upenn.edu/docs/LDC99T42/tagguid1.pdf.

  16. http://languagelog.ldc.upenn.edu/myl/PennTreebank1995.pdf.

  17. https://nlp.stanford.edu/software/stanford-dependencies.shtml.

  18. https://nlp.stanford.edu/software/dependencies_manual.pdf.

  19. amod, aux, auxpass, case, compound, cop, dep, det, dobj, mark, mwe, nmod, nummod, xcomp and or.

  20. DecisionRuleMiner, the developed prototype tool that implements the main stages of our framework, will be described in Sect. 6.

  21. http://www.atlantapd.org/Home/ShowDocument?id=810.

  22. http://docplayer.fr/82860665-Ing-home-family-insurance-general-conditions-tenant.html.

  23. https://www.chubb.com/us-en/terms-of-use.aspx.

  24. https://www.esecutive.com/pdfs/Liability_Insurance_Conditions.pdf.

  25. http://www.fwo.be/en/general-regulations/.

  26. http://eureka-sd-project.eu/general_information?lang=en.

  27. https://www.fifa.com/mm/document/footballdevelopment/refereeing/81/42/36/lawsofthegame_2012_e.pdf.

  28. https://www.airbus.com/content/dam/corporate-topics/corporate-social-responsibility/ethics-and-compliance/Airbus-Ethics-Compliance-Code-Conduct-EN.pdf.

  29. https://www.apple.com/supplier-responsibility/pdf/Apple_SR_2018_Progress_Report.pdf.

  30. https://www.citigroup.com/citi/investor/data/codeconduct_en.pdf.

  31. https://www.beiersdorf.com/investors/corporate-governance/code-of-conduct.

  32. https://ditm-twdc-us.storage.googleapis.com/Manufacturer-Code-of-Conduct-Translations.pdf.

  33. https://www.ibm.com/investor/pdf/BCG_Feb_2011_English_CE.pdf.

  34. https://www.rspo.org/file/acop/lidl-stiftung-cokg/R-Policies-to-PNC-laborrights.pdf.

  35. https://www.pmi.org/-/media/pmi/documents/public/pdf/ethics/pmi-code-of-ethics.pdf.

  36. https://www.home.sandvik/en/about-us/sustainable-business/code-of-conduct/.

  37. https://www.pepsico.com/Assets/Download/CodeOfConduct/English_GCOC_2014.pdf.

  38. https://abc.xyz/investor/other/google-code-of-conduct.html.

  39. https://www.omg.org/news/whitepapers/An_Introduction_to_Decision_Modeling_with_DMNv51-15-15.

  40. https://www.uhasselt.be/Qualtrics.

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Acknowledgements

This research was supported by the special research fund for incoming mobility of Hasselt University, Belgium. The authors gratefully acknowledge Veronika Boyanova and Aziz Yarahmadi for providing useful descriptions in our experiments, as well as the experts who kindly answered the survey.

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Authors and Affiliations

  1. AI Lab, Computer Science Department, Vrije Universiteit Brussel, Pleinlaan 9, 3rd floor, 1050, Brussels, Belgium

    Leticia Arco

  2. Business Informatics Group, Faculty of Business Economics, Hasselt University, Diepenbeek Kantoor A50, Hasselt, Belgium

    Leticia Arco, Gonzalo Nápoles, Frank Vanhoenshoven & Koen Vanhoof

  3. AI Lab, Computer Science Department, Central University of Las Villas, Carretera a Camajuaní km 5 1/2, Santa Clara, Villa Clara, Cuba

    Ana Laura Lara

  4. Weast Coast University, Miami Campus, 9250 NW 36th St, Doral, FL, 33178, USA

    Gladys Casas

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  1. Leticia Arco
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Correspondence to Leticia Arco.

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Arco, L., Nápoles, G., Vanhoenshoven, F. et al. Natural language techniques supporting decision modelers. Data Min Knowl Disc 35, 290–320 (2021). https://doi.org/10.1007/s10618-020-00718-4

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