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Detecting Constraints and Their Relations from Regulatory Documents Using NLP Techniques

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Book cover On the Move to Meaningful Internet Systems. OTM 2018 Conferences (OTM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11229))

Abstract

Extracting constraints and process models from natural language text is an ongoing challenge. While the focus of current research is merely on the extraction itself, this paper presents a three step approach to group constraints as well as to detect and display relations between constraints in order to ease their implementation. For this, the approach uses NLP techniques to extract sentences containing constraints, group them by, e.g., stakeholders or topics, and detect redundant, subsuming, and conflicting pairs of constraints. These relations are displayed using network maps. The approach is prototypically implemented and evaluated based on regulatory documents from the financial sector as well as expert interviews.

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Notes

  1. 1.

    The POS tags are necessary at a later stage of the method.

  2. 2.

    The quality of the outcome of this step relies on the NLP framework that is used.

  3. 3.

    https://spacy.io.

  4. 4.

    https://wordnet.princeton.edu/.

  5. 5.

    https://www.bis.org/publ/bcbs239.pdf.

  6. 6.

    https://eur-lex.europa.eu/eli/reg/2016/867/oj.

  7. 7.

    Another possibility is to use a glossary and carry out the grouping based on the therein contained terms.

References

  1. Aggarwal, C.C., Zhai, C.: Mining Text Data. Springer, New York (2012). https://doi.org/10.1007/978-1-4614-3223-4

    Book  Google Scholar 

  2. Al Omran, F.N.A., Treude, C.: Choosing an NLP library for analyzing software documentation: a systematic literature review and a series of experiments. In: Proceedings of the 14th International Conference on Mining Software Repositories, pp. 187–197. IEEE Press (2017)

    Google Scholar 

  3. de AR Goncalves, J.C., Santoro, F.M., Baiao, F.A.: Business process mining from group stories. In: 13th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2009, pp. 161–166. IEEE (2009)

    Google Scholar 

  4. Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)

    Google Scholar 

  5. Bajwa, I.S., Lee, M.G., Bordbar, B.: SBVR business rules generation from natural language specification. In: AAAI Spring Symposium: AI for Business Agility, pp. 2–8 (2011)

    Google Scholar 

  6. Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media, Inc., Sebastopol (2009)

    MATH  Google Scholar 

  7. Deeptimahanti, D.K., Babar, M.A.: An automated tool for generating UML models from natural language requirements. In: Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering, pp. 680–682. IEEE Computer Society (2009)

    Google Scholar 

  8. Dijkman, R., Dumas, M., Van Dongen, B., Käärik, R., Mendling, J.: Similarity of business process models: metrics and evaluation. Inf. Syst. 36(2), 498–516 (2011)

    Article  Google Scholar 

  9. Dragoni, M., Villata, S., Rizzi, W., Governatori, G.: Combining NLP approaches for rule extraction from legal documents. In: 1st Workshop on MIning and REasoning with Legal texts (MIREL 2016) (2016)

    Google Scholar 

  10. 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 

  11. Ghose, A., Koliadis, G., Chueng, A.: Process discovery from model and text artefacts. In: 2007 IEEE Congress on Services, pp. 167–174. IEEE (2007)

    Google Scholar 

  12. Hagberg, A., Swart, P., Schult, D.: Exploring network structure, dynamics, and function using networkx. Technical report, Los Alamos National Laboratory (LANL), Los Alamos, NM (United States) (2008)

    Google Scholar 

  13. Harabagiu, S., Hickl, A., Lacatusu, F.: Negation, contrast and contradiction in text processing. In: AAAI, vol. 6, pp. 755–762 (2006)

    Google Scholar 

  14. Hashmi, M., Governatori, G., Lam, H.P., Wynn, M.T.: Are we done with business process compliance: state of the art and challenges ahead. Knowl. Inf. Syst. 57, 1–55 (2018)

    Article  Google Scholar 

  15. de Marneffe, M.C., Rafferty, A.R., Manning, C.D.: Identifying conflicting information in texts. In: Handbook of Natural Language Processing and Machine Translation: DARPA Global Autonomous Language Exploitation (2011)

    Google Scholar 

  16. Mikolov, T., Yih, W.T., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 746–751 (2013)

    Google Scholar 

  17. Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.J.: Introduction to wordnet: an on-line lexical database. Int. J. Lexicogr. 3(4), 235–244 (1990)

    Article  Google Scholar 

  18. More, P., Phalnikar, R.: Generating UML diagrams from natural language specifications. Int. J. Appl. Inf. Syst. 1(8), 19–23 (2012)

    Google Scholar 

  19. Nguyen, T.A., Perkins, W.A., Laffey, T.J., Pecora, D.: Checking an expert systems knowledge base for consistency and completeness. In: IJCAI, vol. 85, pp. 375–378 (1985)

    Google Scholar 

  20. Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  21. Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  22. Riefer, M., Ternis, S.F., Thaler, T.: Mining process models from natural language text: a state-of-the-art analysis. In: Multikonferenz Wirtschaftsinformatik (MKWI 2016), pp. 9–11, March 2016

    Google Scholar 

  23. Rinderle-Ma, S., Ma, Z., Madlmayr, B.: Using content analysis for privacy requirement extraction and policy formalization. In: Enterprise Modelling and Information Systems Architectures, pp. 93–107 (2015)

    Google Scholar 

  24. 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)

    Article  Google Scholar 

  25. Sinha, A., Paradkar, A.: Use cases to process specifications in business process modeling notation. In: 2010 IEEE International Conference on Web Services (ICWS), pp. 473–480. IEEE (2010)

    Google Scholar 

  26. Winter, K., Rinderle-Ma, S., Grossmann, W., Feinerer, I., Ma, Z.: Characterizing regulatory documents and guidelines based on text mining. In: Panetto, H. (ed.) OTM 2017. LNCS, vol. 10573, pp. 3–20. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69462-7_1

    Chapter  Google Scholar 

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Acknowledgment

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

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Authors and Affiliations

  1. Faculty of Computer Science, University of Vienna, Vienna, Austria

    Karolin Winter & Stefanie Rinderle-Ma

Authors
  1. Karolin Winter
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  2. Stefanie Rinderle-Ma
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Corresponding author

Correspondence to Karolin Winter .

Editor information

Editors and Affiliations

  1. CNRS, University of Lorraine, Vandoeuvre-les-Nancy, France

    Hervé Panetto

  2. Trinity College Dublin, Dublin, Ireland

    Christophe Debruyne

  3. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Henderik A. Proper

  4. Università degli Studi di Milano, Crema, Italy

    Claudio Agostino Ardagna

  5. SINTEF and University of Oslo, Oslo, Norway

    Dumitru Roman

  6. TU Graz, Graz, Austria

    Robert Meersman

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Winter, K., Rinderle-Ma, S. (2018). Detecting Constraints and Their Relations from Regulatory Documents Using NLP Techniques. In: Panetto, H., Debruyne, C., Proper, H., Ardagna, C., Roman, D., Meersman, R. (eds) On the Move to Meaningful Internet Systems. OTM 2018 Conferences. OTM 2018. Lecture Notes in Computer Science(), vol 11229. Springer, Cham. https://doi.org/10.1007/978-3-030-02610-3_15

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  • DOI: https://doi.org/10.1007/978-3-030-02610-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02609-7

  • Online ISBN: 978-3-030-02610-3

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