Towards Linked Data for Ecosystems of Digital Twins
Samuele Burattini, Antoine Zimmermann, Marco Picone, Alessandro Ricci · 2024
Due to either the inherent complexity of the domain or the evolving nature of systems, we can envision solutions that digitalize assets in a complex domain using an ecosystem of distributed Digital Twins instead of a single monolithic one. To effectively tackle interoperability in such ecosystems, this paper advocates for the introduction of a representation based on Semantic Web technologies enabling the discovery of both Digital Twin structure - i.e. the static information about the asset model and offered services - and state - i.e. the data and metrics collected at runtime - to support the management of ecosystems and the creation of application mashups. A review of the state of the art suggests that currently investigated ways to describe a Digital Twin are not sufficient to achieve this objective. A proposal of key requirements for a Digital Twin representation is outlined leading to the proposal of a core ontology and a Linked Data approach for state management.