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.

该研究论文介绍了在数字孪生系统中利用语义关联规则学习和知识图谱的方法，并通过工业应用的评估表明该方法能够学习具有语义信息的大量关联规则，为进一步研究工业应用的语义关联规则学习奠定了基础。