# Automated Knowledge Graph Learning in Industrial Processes

arXiv: https://arxiv.org/abs/2407.02106 · HTML: https://mlsanity.com/paper/2407.02106/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Builds on (most cited first, 5 of 5)

- [Granger Causality: A Review and Recent Advances](https://mlsanity.com/paper/2105.02675/) (2021)
- [Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review](https://mlsanity.com/paper/2012.09049/) (2020)
- [XAI-KG: knowledge graph to support XAI and decision-making in manufacturing](https://mlsanity.com/paper/2105.01929/) (2021)
- [Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use Cases](https://mlsanity.com/paper/1808.00434/) (2018)
- [Unveiling Relations in the Industry 4.0 Standards Landscape based on Knowledge Graph Embeddings](https://mlsanity.com/paper/2006.04556/) (2020)

## Built on by (most cited first, 1 of 1)

- [An Agentic Framework for Rapid Deployment of Edge AI Solutions in Industry 5.0](https://mlsanity.com/paper/2510.25813/) (2025)

## Abstract (arXiv)

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.