  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.
