通用数字孪生——动态知识图谱

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2021-09-06 DOI:10.1017/dce.2021.10
J. Akroyd, S. Mosbach, A. Bhave, M. Kraft
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引用次数: 37

摘要

摘要:本文介绍了一种数字孪生的动态知识图方法,并说明了这种方法如何在设计上自然地适合于实现通用数字孪生的愿景。动态知识图谱是利用语义网技术实现的。它由使用本体定义的概念和实例以及在概念和实例上操作以更新动态知识图的计算代理组成。通过构造,它是分布式的,支持跨域互操作性,并确保数据通过统一接口连接、可移植、可发现和可查询。知识图谱包括“基础世界”和“平行世界”的概念,前者描述了真实世界,并由整合实时数据的代理维护;后者支持在不影响基础世界的情况下对可选设计进行智能探索。用例展示了动态知识图承载地理空间和化学数据、控制化学实验、执行跨域模拟和执行场景分析的能力。如何为备选方案提出明智的建议,以及如何确保知识图所考虑的方案与社会目标之间的一致性。讨论了扩展动态知识图谱以开发英国数字孪生的工作,以支持能源系统的脱碳。指出了今后研究的重要方向。
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Universal Digital Twin - A Dynamic Knowledge Graph
Abstract This 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.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
期刊最新文献
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