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

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Advanced Information Systems Engineering (CAiSE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11483))

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Abstract

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.

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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

  2. Data Science @ Uni Vienna, University of Vienna, Vienna, Austria

    Stefanie Rinderle-Ma

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

Correspondence to Stefanie Rinderle-Ma .

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Editors and Affiliations

  1. University of Trento, Trento, Italy

    Paolo Giorgini

  2. University of St. Gallen, St. Gallen, Switzerland

    Barbara Weber

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Winter, K., Rinderle-Ma, S. (2019). Deriving and Combining Mixed Graphs from Regulatory Documents Based on Constraint Relations. In: Giorgini, P., Weber, B. (eds) Advanced Information Systems Engineering. CAiSE 2019. Lecture Notes in Computer Science(), vol 11483. Springer, Cham. https://doi.org/10.1007/978-3-030-21290-2_27

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  • DOI: https://doi.org/10.1007/978-3-030-21290-2_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21289-6

  • Online ISBN: 978-3-030-21290-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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