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A Concept for Generating Business Process Models from Natural Language Description

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Knowledge Science, Engineering and Management (KSEM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11061))

Abstract

Manual extraction of business process models from technical documentation is a time-consuming task. Several approaches to generating such process models have been proposed. We present a proposal of a new method for extracting business process from natural language text through intermediate process model using the spreadsheet-based representation. Such intermediate model is transformed into a valid BPMN process model. Our method is enhanced with semantic analysis of the text, allows for quick check of the transformation result and manual correction during this process. As the obtained BPMN model is structured, it is easier to check its correctness.

The paper is supported by the AGH UST research grant.

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Notes

  1. 1.

    For the sake of clarity, we focus on the four main properties of this representation.

  2. 2.

    See: https://github.com/KrzyHonk/bpmn-python.

  3. 3.

    See: https://bpmai.org/BPMAcademicInitiative/CreateProcessModels.

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Authors and Affiliations

  1. AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059, Krakow, Poland

    Krzysztof Honkisz, Krzysztof Kluza & Piotr Wiśniewski

Authors
  1. Krzysztof Honkisz
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  2. Krzysztof Kluza
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  3. Piotr Wiśniewski
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Corresponding author

Correspondence to Krzysztof Kluza .

Editor information

Editors and Affiliations

  1. University of Bristol, Bristol, United Kingdom

    Weiru Liu

  2. Università di Trento, Povo, Italy

    Fausto Giunchiglia

  3. Jilin University, Changchun, China

    Bo Yang

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Honkisz, K., Kluza, K., Wiśniewski, P. (2018). A Concept for Generating Business Process Models from Natural Language Description. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11061. Springer, Cham. https://doi.org/10.1007/978-3-319-99365-2_8

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  • DOI: https://doi.org/10.1007/978-3-319-99365-2_8

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  • Online ISBN: 978-3-319-99365-2

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