Framework for an Open-Access Dynamic Accident Knowledge Graph Platform for the Critical Infrastructures in Process Industry

The resilience of Critical Infrastructures (CIs) in the process industry is fundamental to the common good of the environment, economy, and society. Hazard identification is the starting point of risk and resilience management. However, state-of-the-art techniques like HAZOP and FMEA highly depend on the expert group's subjective judgment. Business managers complain about the multiple safety certificates required from different regulators, as the hazard identifications from various groups are inconsistent and do not recognize each other.
There are already many established accident report databases. However, they only provide information and other knowledge about the accident hazards data and are usually unstructured. Thanks to the development of large language models, this research aims to propose a framework to connect and extract evidence-based hazard information from these databases and keep updating the data at season intervals. The extracted data could be structured into a dynamic Knowledge Graph (KG) according to the different functions of CIs and the category of the hazards. An example of KG based on the Major Accident Reporting System (eMARS) dataset is developed as a case study.

Keywords: Critical infrastructure, Resilience, Hazard identification, Knowledge graph.