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Universal Digital Twin - A Dynamic Knowledge Graph

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Akroyd, J 
Bhave, A 


jats:titleAbstract</jats:title>jats:pThis paper introduces a dynamic knowledge-graph approach for digital twins and illustrates how this approach is by design naturally suited to realizing the vision of a Universal Digital Twin. The dynamic knowledge graph is implemented using technologies from the Semantic Web. It is composed of concepts and instances that are defined using ontologies, and of computational agents that operate on both the concepts and instances to update the dynamic knowledge graph. By construction, it is distributed, supports cross-domain interoperability, and ensures that data are connected, portable, discoverable, and queryable via a uniform interface. The knowledge graph includes the notions of a “base world” that describes the real world and that is maintained by agents that incorporate real-time data, and of “parallel worlds” that support the intelligent exploration of alternative designs without affecting the base world. Use cases are presented that demonstrate the ability of the dynamic knowledge graph to host geospatial and chemical data, control chemistry experiments, perform cross-domain simulations, and perform scenario analysis. The questions of how to make intelligent suggestions for alternative scenarios and how to ensure alignment between the scenarios considered by the knowledge graph and the goals of society are considered. Work to extend the dynamic knowledge graph to develop a digital twin of the UK to support the decarbonization of the energy system is discussed. Important directions for future research are highlighted.</jats:p>



Agents, data, digital twin, dynamic knowledge graph, interoperability

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Data-Centric Engineering

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Cambridge University Press (CUP)
This research was supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. Part of the research was also funded by the European Commission, Horizon 2020 Programme, DOME 4.0 Project, GA 953163. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Additional support for a number of PhD studentships was provided by Computational Modelling Cambridge Ltd. M.K. gratefully acknowledges the support of the Alexander von Humboldt Foundation.