Digital Twins are digital representations of systems in the Internet of
Things (IoT) that are often based on AI models that are trained on data from
those systems. Semantic models are used increasingly to link these datasets
from different stages of the IoT systems life-cycle together and to
automatically configure the AI modelling pipelines. This combination of
semantic models with AI pipelines running on external datasets raises unique
challenges particular if rolled out at scale. Within this paper we will discuss
the unique requirements of applying semantic graphs to automate Digital Twins
in different practical use cases. We will introduce the benchmark dataset DTBM
that reflects these characteristics and look into the scaling challenges of
different knowledge graph technologies. Based on these insights we will propose
a reference architecture that is in-use in multiple products in IBM and derive
lessons learned for scaling knowledge graphs for configuring AI models for
Digital Twins.

本文讨论了在不同的实际应用中将语义图应用于自动化数字孪生所需的独特要求，并介绍了反映这些特征的基准数据集 DTBM，并探讨了不同知识图技术的规模化挑战。基于这些见解，我们将提出在 IBM 中多个产品中使用的参考架构，并推导出用于配置数字孪生的 AI 模型的规模化知识图的教训。