论文标题
缩放知识图，以自动化数字双胞胎的AI
Scaling Knowledge Graphs for Automating AI of Digital Twins
论文作者
论文摘要
数字双胞胎是物联网（IoT）中系统的数字表示，这些数字表示通常基于对这些系统数据进行培训的AI模型。语义模型越来越多地将这些数据集从物联网系统生命周期的不同阶段链接在一起，并自动配置AI建模管道。这种语义模型与AI管道在外部数据集上运行的组合将引起独特的挑战，特别是按大规模推出。在本文中，我们将讨论在不同实际用例中使用语义图以自动化数字双胞胎的独特要求。我们将介绍反映这些特征的基准数据集DTBM，并研究不同知识图技术的缩放挑战。基于这些见解，我们将提出一种参考体系结构，该参考体系结构在IBM中的多个产品中使用，并得出了用于扩展知识图的经验教训，以配置数字双胞胎的AI模型。
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.
