# Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2012.09049 · HTML: https://mlsanity.com/paper/2012.09049/

## Key facts

- Cited by 8 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [XAI-KG: knowledge graph to support XAI and decision-making in manufacturing](https://mlsanity.com/paper/2105.01929/) (2021)
- [Automated Knowledge Graph Learning in Industrial Processes](https://mlsanity.com/paper/2407.02106/) (2024)
- [Rethinking industrial artificial intelligence: a unified foundation framework](https://mlsanity.com/paper/2504.01797/) (2025)
- [Scalable Uncertainty Reasoning in Knowledge Graphs](https://mlsanity.com/paper/2605.16568/) (2026)
- [FabKG: A Knowledge graph of Manufacturing Science domain utilizing structured and unconventional unstructured knowledge source](https://mlsanity.com/paper/2206.10318/) (2022)
- [PDPK: A Framework to Synthesise Process Data and Corresponding Procedural Knowledge for Manufacturing](https://mlsanity.com/paper/2308.08371/) (2023)
- [A Survey on Recent Random Walk-based Methods for Embedding Knowledge Graphs](https://mlsanity.com/paper/2406.07402/) (2024)
- [An Agentic Framework for Rapid Deployment of Edge AI Solutions in Industry 5.0](https://mlsanity.com/paper/2510.25813/) (2025)

## Abstract (arXiv)

Knowledge graphs in manufacturing and production aim to make production lines more efficient and flexible with higher quality output. This makes knowledge graphs attractive for companies to reach Industry 4.0 goals. However, existing research in the field is quite preliminary, and more research effort on analyzing how knowledge graphs can be applied in the field of manufacturing and production is needed. Therefore, we have conducted a systematic literature review as an attempt to characterize the state-of-the-art in this field, i.e., by identifying exiting research and by identifying gaps and opportunities for further research. To do that, we have focused on finding the primary studies in the existing literature, which were classified and analyzed according to four criteria: bibliometric key facts, research type facets, knowledge graph characteristics, and application scenarios. Besides, an evaluation of the primary studies has also been carried out to gain deeper insights in terms of methodology, empirical evidence, and relevance. As a result, we can offer a complete picture of the domain, which includes such interesting aspects as the fact that knowledge fusion is currently the main use case for knowledge graphs, that empirical research and industrial application are still missing to a large extent, that graph embeddings are not fully exploited, and that technical literature is fast-growing but seems to be still far from its peak.