Semantic data modeling can be defined as the development of descriptions and representations of data in such a way that the latter’s meaning is explicit, accurate, and commonly understood by both humans and computer systems. This definition encompasses a wide range of data artifacts, including metadata schemas, controlled vocabularies, taxonomies, ontologies, knowledge graphs, entity-relationship (E-R) models, property graphs, and other conceptual models for data representation.
That’s a great summary. One of the most overlooked aspects of semantic modeling is that it applies to dynamic systems as well as statically-modeled concepts. Temporal graphs fits neatly into both (transient and point-in-time) and so do communications protocols. In fact, the OSI 7-layer model and accompanying OSI/X-CCITT standards provides one of the best examples of how semantics facilitate communications across complex distributed environments and with clear separations covering: transmission, negotiation, encoding, session, and application level concerns. We take this pretty much for granted but our present-day interconnectedness would not exist without these differing semantic interacting layers at work. This model also illustrates how to break a complex model down into separately-manageable and discrete sub-models that work together as a whole.