Converting natural language policy article into MBSE model

Abstract

AbstractSystem Engineers work on designing human interfaces for easy access to governance and technology. As we move to digital engineering, the policy documents are still written in natural language that sometimes is obscure and verbose. One way of digitizing is to convert natural language policies into machine readable system engineering models. In the past, converting natural language written policy documents into machine readable models involves great human effort and expert knowledge in relevant domains, which is a time consuming, tedious, and sometimes impossible task. Artificial Intelligence and its application have shown the potential to accelerate the process. In this study, we proposed a natural language processing (NLP)‐based framework for information extraction under the general condition that can automatically detect the actors and their responsible actions. To validate the performance of the model developed, we compared the NLP generated report with manually created SysML model. The result shows that the precision and recall rate of extracting roles and responsibility is ~0.86 and ~0.66, respectively, representing that this text‐to‐model framework has the potential to accurately convert general policy documents into SysML.