Due to increasingly required flexibility in manufacturing systems, adaptation of monitoring and control to changing context such as reconfiguration of devices becomes more important. Referring to the usage of structured information on the Web, digital twin models of manufacturing data can be seen as knowledge graphs that constantly need to be aligned with the physical environment. With a growing number of smart devices participating in production processes, handling these alignments manually is no longer feasible. Yet, the growing availability of data coming from operations (e.g. process events) and contextual sources (e.g. equipment configurations) enables machine learning to synchronize data models with physical reality. Common knowledge graph learning approaches, however, are not designed to deal with both, static and time-dependent data.In order to overcome this, we introduce a representation learning model that shows promising results for the synchronization of semantics from existing manufacturing knowledge graphs and operational data.