The classical understanding of spacetime from Newton's era is based on ballistics, the idea about space and time was that of a purely passive theatre for the motion and behaviours of material bodies.
[5] Einstein partially changed that perception with General Relativity, in which spacetime geometry is an active participant with its own properties, i.e. curvature, energy, and mass.
In the process models of Computer Science, Electronics, Biology, and Logistics, however, space is formed from functional components that act more like service providers.
[6] Processes are representations of autonomous modular outcomes, a result of information passing between agents in networks of such active components, with a certain strength of coupling.
Burgess also observed a relationship between semantic knowledge representations and the bigraphs of Robin Milner, but found existing languages excessively formal and lacking in expressibility.
Classically, the role is separated from space and time, but this may add layers of unwanted complexity as there are hidden assumptions behind a model of spacetime.
Burgess describes Semantic Spacetime as an idea in its infancy, with much work left to do,[7] attracting a small amount of interest mainly from deep specialists.
Semantic Spacetime model and Promise Theory were references as an approach to multi-model database design and Resource Description Framework embedding for ArangoDB.
[10] Limited papers on smart data pipelines and consistent propagation of information have been based on semantic spacetime and led to startups Aljabr and Dianemo [11][12] to develop the respective technologies.
[13] Applications of the model to neuroscience and machine learning were recognized by an invitation to a special closed event salon in October 2022 by the Kavli Foundation (United States).