# Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use Cases

arXiv: https://arxiv.org/abs/1808.00434 · HTML: https://mlsanity.com/paper/1808.00434/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [Graph Modeling in Computer Assisted Automotive Development](https://mlsanity.com/paper/2209.14910/) (2022)
- [Automated Knowledge Graph Learning in Industrial Processes](https://mlsanity.com/paper/2407.02106/) (2024)
- [FabKG: A Knowledge graph of Manufacturing Science domain utilizing structured and unconventional unstructured knowledge source](https://mlsanity.com/paper/2206.10318/) (2022)
- [Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding](https://mlsanity.com/paper/2209.11067/) (2022)

## Abstract (arXiv)

Industry is evolving towards Industry 4.0, which holds the promise of increased flexibility in manufacturing, better quality and improved productivity. A core actor of this growth is using sensors, which must capture data that can used in unforeseen ways to achieve a performance not achievable without them. However, the complexity of this improved setting is much greater than what is currently used in practice. Hence, it is imperative that the management cannot only be performed by human labor force, but part of that will be done by automated algorithms instead. A natural way to represent the data generated by this large amount of sensors, which are not acting measuring independent variables, and the interaction of the different devices is by using a graph data model. Then, machine learning could be used to aid the Industry 4.0 system to, for example, perform predictive maintenance. However, machine learning directly on graphs, needs feature engineering and has scalability issues. In this paper we discuss methods to convert (embed) the graph in a vector space, such that it becomes feasible to use traditional machine learning methods for Industry 4.0 settings.