Deriving and Combining Mixed Graphs from Regulatory Documents Based on Constraint Relations

  • Karolin Winter
  • Stefanie Rinderle-MaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11483)


Extracting meaningful information from regulatory documents such as the General Data Protection Regulation (GDPR) is of utmost importance for almost any company. Existing approaches pose strict assumptions on the documents and output models containing inconsistencies or redundancies since relations within and across documents are neglected. To overcome these shortcomings, this work aims at deriving mixed graphs based on paragraph embedding as well as process discovery and combining these graphs using constraint relations such as “redundant” or “conflicting” detected by the ConRelMiner method. The approach is implemented and evaluated based on two real-world use cases: Austria’s energy use cases plus the contained process models as ground truth and the GDPR. Mixed graphs and their combinations constitute the next step towards an end-to-end solution for extracting process models from text, either from scratch or amending existing ones.


Regulatory documents Constraint extraction Text mining NLP Process discovery 



This work has been funded by the Vienna Science and Technology Fund (WWTF) through project ICT15-072.


  1. 1.
    Smart metering use-cases für das advanced meter communication system (AMCS), version 1.0. Technical report 1/88, Österreichs Energie (2015)Google Scholar
  2. 2.
    Van der Aa, H., Carmona Vargas, J., Leopold, H., Mendling, J., Padró, L.: Challenges and opportunities of applying natural language processing in business process management. In: Computational Linguistics, pp. 2791–2801 (2018)Google Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    van der Aa, H., Leopold, H., Reijers, H.A.: Checking process compliance against natural language specifications using behavioral spaces. Inf. Syst. 78, 83–95 (2018)CrossRefGoogle Scholar
  5. 5.
    Allen, F.E.: Control flow analysis. In: ACM SIGPLAN Notices, vol. 5, pp. 1–19 (1970)CrossRefGoogle Scholar
  6. 6.
    de AR Goncalves, J.C., Santoro, F.M., Baiao, F.A.: Business process mining from group stories. In: International Conference on Computer Supported Cooperative Work in Design, pp. 161–166 (2009)Google Scholar
  7. 7.
    Bajwa, I.S., Lee, M.G., Bordbar, B.: SBVR business rules generation from natural language specification. In: AAAI Spring Symposium, pp. 2–8 (2011)Google Scholar
  8. 8.
    Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media, Inc., Massachusetts (2009)zbMATHGoogle Scholar
  9. 9.
    Deeptimahanti, D.K., Babar, M.A.: An automated tool for generating UML models from natural language requirements. In: Automated Software Engineering, pp. 680–682 (2009)Google Scholar
  10. 10.
    Dragoni, M., Villata, S., Rizzi, W., Governatori, G.: Combining NLP approaches for rule extraction from legal documents. In: MIning and REasoning with Legal Texts (2016)Google Scholar
  11. 11.
    Friedrich, F., Mendling, J., Puhlmann, F.: Process model generation from natural language text. In: Advanced Information Systems Engineering, pp. 482–496 (2011)Google Scholar
  12. 12.
    Ghose, A., Koliadis, G., Chueng, A.: Process discovery from model and text artefacts. In: Services, pp. 167–174 (2007)Google Scholar
  13. 13.
    Group, I.E.W., et al.: ICH harmonized tripartite guideline, quality risk management q9. In: Technical Requirements for Registration of Pharmaceuticals for Human Use (2005)Google Scholar
  14. 14.
    Hansen, P., Kuplinsky, J., de Werra, D.: Mixed graph colorings. Math. Methods Oper. Res. 45(1), 145–160 (1997)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Kabicher, S., Rinderle-Ma, S.: Human-centered process engineering based on content analysis and process view aggregation. In: Advanced Information Systems Engineering, pp. 467–481 (2011)Google Scholar
  16. 16.
    Ly, L.T., Maggi, F.M., Montali, M., Rinderle-Ma, S., van der Aalst, W.M.P.: Compliance monitoring in business processes: functionalities, application, and tool-support. Inf. Syst. 54, 209–234 (2015)CrossRefGoogle Scholar
  17. 17.
    More, P., Phalnikar, R.: Generating UML diagrams from natural language specifications. Appl. Inf. Syst. 1(8), 19–23 (2012)Google Scholar
  18. 18.
    Ren, P., Chen, Z., Ren, Z., Wei, F., Ma, J., de Rijke, M.: Leveraging contextual sentence relations for extractive summarization using a neural attention model. In: Research and Development in Information Retrieval, pp. 95–104 (2017)Google Scholar
  19. 19.
    Riefer, M., Ternis, S.F., Thaler, T.: Mining process models from natural language text: a state-of-the-art analysis. Multikonferenz Wirtschaftsinformatik, pp. 9–11 (2016)Google Scholar
  20. 20.
    Saha, T.K., Joty, S., Hassan, N., Hasan, M.A.: Regularized and retrofitted models for learning sentence representation with context. In: Information and Knowledge Management, pp. 547–556 (2017)Google Scholar
  21. 21.
    Selway, M., Grossmann, G., Mayer, W., Stumptner, M.: Formalising natural language specifications using a cognitive linguistic/configuration based approach. Inf. Syst. 54, 191–208 (2015)CrossRefGoogle Scholar
  22. 22.
    Sinha, A., Paradkar, A.: Use cases to process specifications in business process modeling notation. In: Web Services, pp. 473–480 (2010)Google Scholar
  23. 23.
    Wang, H.J., Zhao, J.L., Zhang, L.J.: Policy-driven process mapping (PDPM): discovering process models from business policies. DSS 48(1), 267–281 (2009)Google Scholar
  24. 24.
    Winter, K., Rinderle-Ma, S.: Detecting constraints and their relations from regulatory documents using NLP techniques. In: On the Move to Meaningful Internet Systems, pp. 261–278 (2018)Google Scholar
  25. 25.
    Winter, K., Rinderle-Ma, S.: Untangling the GDPR using ConRelMiner. arXiv:1811.03399 (2018)
  26. 26.
    Winter, K., Rinderle-Ma, S., Grossmann, W., Feinerer, I., Ma, Z.: Characterizing regulatory documents and guidelines based on text mining. In: On the Move to Meaningful Internet Systems, pp. 3–20 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Computer ScienceUniversity of ViennaViennaAustria
  2. 2.Data Science @ Uni ViennaUniversity of ViennaViennaAustria

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