Digital Twins (DT) are a promising concept in cyber-physical systems research due to their advanced features including monitoring and automated reasoning. Semantic technologies such as Knowledge Graphs (KG) are recently being utilized in DTs especially for information modelling. Building on this move, this paper proposes a pipeline for semantic association rule learning in DTs using KGs and time series data. In addition to this initial pipeline, we also propose new semantic association rule criterion. The approach is evaluated on an industrial water network scenario. Initial evaluation shows that the proposed approach is able to learn a high number of association rules with semantic information which are more generalizable. The paper aims to set a foundation for further work on using semantic association rule learning especially in the context of industrial applications.

翻译：数字孪生（Digital Twins, DT）因其具备监控与自动推理等先进特性，成为信息物理系统研究中富有前景的概念。近年来，知识图谱（Knowledge Graph, KG）等语义技术正被广泛应用于数字孪生，尤其在信息建模领域。基于这一趋势，本文提出了一种利用知识图谱与时间序列数据在数字孪生中进行语义关联规则学习的流水线方法。除初始流水线外，我们还提出了新型语义关联规则评判标准。该方法在工业水网场景中进行了评估。初步评估表明，所提方法能够学习大量包含语义信息且更具泛化性的关联规则。本文旨在为语义关联规则学习（尤其在工业应用中）的进一步研究奠定基础。