Automated Knowledge Graph Learning in Industrial Processes

TL;DR

This paper presents a framework for automatically converting industrial time series data into knowledge graphs, enhancing decision-making and process optimization by revealing causal relationships and key attributes.

Contribution

It introduces a novel framework tailored for industrial data that automates knowledge graph learning and employs Granger causality for insightful analysis.

Findings

Improved process understanding through knowledge graphs

Identification of causal influences in industrial data

Enhanced predictive model design insights

Abstract

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.