Enhancing the efficiency of product development in manufacturing is vital for maintaining both competitiveness and profitability. In recent years, the adoption of Model-Based Systems Engineering (MBSE) has accelerated as a way to cope with increasingly large and complex systems. Yet constructing system models in the Systems Modeling Language (SysML) demands significant effort, specialized knowledge, and skills—resources that are currently in short supply. This study reports on the development of a method that embeds, in prompts for generative AI, an ontology describing the concepts to be extracted and their interrelationships, along with a small set of concrete examples, enabling the high-precision extraction of the model elements desired by the user.
ARIMA et al. (Wed,) studied this question.