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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)

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.

Keywords

Regulatory documents Constraint extraction Text mining NLP Process discovery 

Notes

Acknowledgment

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

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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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