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.