  Industrial processes generate vast amounts of time series data, yet
extracting meaningful relationships and insights remains challenging. This
paper introduces a framework for automated knowledge graph learning from time
series data, specifically tailored for industrial applications. Our framework
addresses the complexities inherent in industrial datasets, transforming them
into knowledge graphs that improve decision-making, process optimization, and
knowledge discovery. Additionally, it employs Granger causality to identify key
attributes that can inform the design of predictive models. To illustrate the
practical utility of our approach, we also present a motivating use case
demonstrating the benefits of our framework in a real-world industrial
scenario. Further, we demonstrate how the automated conversion of time series
data into knowledge graphs can identify causal influences or dependencies
between important process parameters.
