Towards a Knowledge Graph Enhanced Automation and Collaboration Framework for Digital Twins

The Digital Twin (DT) provides a digital representation of a physical system and allows users to interactively study the physical processes of a real system via the digital representation in different scenarios in real time. The development of a DT is highly complex; it requires not only expertise from multiple disciplines but also the integration of often heterogeneous software components, e.g., simulations, machine learning, visualization, and user interface components across distributed environments. This poster presents a Knowledge Graph-based ontological framework to boost automation and collaboration during the DT lifecycle stages. We implement our methods in developing a what-if analysis service for a DT of an ecosystem of wetlands and its automated deployment to the Amazon Web Services (AWS) cloud.