Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use
  Cases
  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.
