Extracting Decision Model Components from Natural Language Text for Automated Business Decision Modelling

The decision model in the DMN (Decision Model and Notation) standard is a declarative representation of decision knowledge, which is favored across industry and academia to represent operational decisions. Many current modeling approaches rely on a) a human modeler, which is a costly, time-consuming approach and it struggles to keep up with domain changes, and b) a lot of data logs, to apply automated modeling, which is not feasible for all domains due to unavailability of data. Furthermore, natural language is a standard and convenient way to document decision knowledge in organizations such as rules, policies, and regulations. Despite such vast availability, decision knowledge extraction from the text is relatively new in this domain. This research investigates state-of-the-art NLP techniques, Rule-based approaches, and ML-based approaches in relevant domains. We provide a general framework, Text2DMN, to automatically convert the decision descriptions to the Decision Models. Using this approach, we aim to support decision modelers by reducing the cost and time of the modeling process. This approach also allows improving the quality of models generated, guided by domain expert knowledge as heuristics. We also discuss some of the challenges of this research.