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.

Top