Integrating data sources and connecting participants in heterogeneous environments is a challenging task that requires extensive expert knowledge about the nature of the systems involved. Even when this knowledge is given in the form of manuals or code documentation, or can be derived by a human expert by interpreting the source code, putting this knowledge to use to actually integrate data sources is mostly entirely left to the human and not exploitable by the system itself. Established standards, such as data spaces or the Asset Administration Shell, aim to provide support with connecting participants in heterogeneous environments, but often fall short when it comes to intuitive interoperability. With this work, we propose an approach to augment existing systems with machine usable expert knowledge via so-called semantic support points: a concept for minimal implementations, adding the power of Semantic Web technologies like semantic queries, as well as the possibility to model expert knowledge in ontologies, to existing systems. One core goal of ours is leaving existing standards untouched, like secure communication between data space participants or industry environments described by Asset Administration Shells, and adding semantic information as an optional, volatile layer on top, generated by externally managed expert knowledge in the form of semantic transformation rules.