Process Knowledge

Process knowledge research focuses on integrating structured domain expertise into artificial intelligence models to improve performance, explainability, and safety, particularly in complex tasks like process prediction and decision-making. Current research emphasizes incorporating this knowledge through various methods, including attention mechanisms in neural networks, knowledge graphs for data governance, and explicit modeling of safety constraints and procedural guidelines within large language models. This work is significant because it addresses limitations of purely data-driven AI, leading to more reliable, interpretable, and trustworthy systems with applications in diverse fields such as healthcare, manufacturing, and data management.