  Modern large-scale automation systems integrate thousands to hundreds of
thousands of physical sensors and actuators. Demands for more flexible
reconfiguration of production systems and optimization across different
information models, standards and legacy systems challenge current system
interoperability concepts. Automatic semantic translation across information
models and standards is an increasingly important problem that needs to be
addressed to fulfill these demands in a cost-efficient manner under constraints
of human capacity and resources in relation to timing requirements and system
complexity. Here we define a translator-based operational interoperability
model for interacting cyber-physical systems in mathematical terms, which
includes system identification and ontology-based translation as special cases.
We present alternative mathematical definitions of the translator learning task
and mappings to similar machine learning tasks and solutions based on recent
developments in machine learning. Possibilities to learn translators between
artefacts without a common physical context, for example in simulations of
digital twins and across layers of the automation pyramid are briefly
discussed.
