Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review

TL;DR

This systematic literature review analyzes knowledge graphs in manufacturing and production to map the current state and identify gaps. Using Kitchenham-guided methods, it screened 833 items and selected 24 primary studies (2016–2020), classifying them by bibliometrics, research type, KG characteristics, and application scenarios. The findings show knowledge fusion as the dominant use case, a predominance of top-down RDF-based KGs, and limited empirical validation or industrial deployment, with embeddings underutilized. The study highlights open challenges—numeric/tabular data integration, real-time KG updates, cross-graph linkages, domain-wide coverage, and best-practice architectures—and advocates broader domain coverage and mature evaluation to accelerate industrial impact.

Abstract

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.

Figures (12)

• Figure I: Primary study selection process
• Figure II: Distribution of primary studies by year
• Figure III: Distribution of primary studies by country
• Figure IV: Distribution of primary studies by forum
• Figure V: Distribution of primary studies by Scimago research field