Universal Digital Twin - A Dynamic Knowledge Graph

Jethro Akroyd,Amit Bhave,Markus Kraft,Sebastian Mosbach

doi:10.1017/dce.2021.10

Abstract

AbstractThis 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.

Highlights

Impact Statement Countries must make significant changes to their infrastructure and energy systems to meet their obligations under the 2016 Paris Agreement, which requires a comprehensive, interdisciplinary approach
We demonstrate how a comprehensive digital twin can be implemented as a dynamic knowledge graph using technologies from the Semantic Web
These factors are strongly interconnected and it is widely appreciated that interoperable (Capellán-Pérez et al, 2020), collaborative (DeCarolis et al, 2020) models that span multiple disciplines (Jain et al, 2017) are necessary, perhaps in the form of digital twins supported by artificial intelligence (AI) (Inderwildi et al, 2020)

Summary

Introduction

Impact Statement Countries must make significant changes to their infrastructure and energy systems to meet their obligations under the 2016 Paris Agreement, which requires a comprehensive, interdisciplinary approach. The development of solutions to these challenges that are inherently interdisciplinary requires the consideration of economic, engineering, environmental, and social factors (Debnath and Mourshed, 2018; Levi et al, 2019; Spyrou et al, 2019) over a range of geographic scales (Yalew et al, 2020) It may require the development of new concepts, for example to facilitate the consideration of societal practices and patterns of consumption in the analysis of different solutions (Shove, 2021). The benefit of a data-centric approach, within which we include the availability of data from smart infrastructure and the sharing of data in a national-scale digital twin, lies in the use of data to support better decision making In mature economies such as the UK, the value of existing infrastructure far exceeds the value of infrastructure that is under development, such that the key benefits will derive from optimizing the use of existing assets (Cambridge Centre for Smart Infrastructure and Construction [CISL], 2016). They raise challenging questions about the ownership, curation, quality, and security of data, about the protection of intellectual property, and about the interoperabililty of data and models, but intentionally avoid prescribing solutions

Objectives

Findings

Conclusion