  The increasing digitalization of the manufacturing domain requires adequate
knowledge modeling to capture relevant information. Ontologies and Knowledge
Graphs provide means to model and relate a wide range of concepts, problems,
and configurations. Both can be used to generate new knowledge through
deductive inference and identify missing knowledge. While digitalization
increases the amount of data available, much data is not labeled and cannot be
directly used to train supervised machine learning models. Active learning can
be used to identify the most informative data instances for which to obtain
users' feedback, reduce friction, and maximize knowledge acquisition. By
combining semantic technologies and active learning, multiple use cases in the
manufacturing domain can be addressed taking advantage of the available
knowledge and data.
