Abstract Agent Type System with Context Graphs, DIDs/VCs, Causality, and Provenance
A comprehensive framework for defining agent behavioral contracts (Abstract Agent Types) and managing their runtime interactions through Context Graphs, with full support for decentralized identity, verifiable credentials, causal reasoning, and provenance tracking.
with property graphs, as compared to RDF, wouldnt the internals of nodes (properties) not be considered actually a part of the graph itself (or atleast not first class)?
this doesn't make any sense though, your write up starts with an assumption that multiple records in the dataset are "obviously" the same entity ... so we wouldn't even need entity resolution then ...
"entity resolution" as a process should moreso imply how to determine whether two records that aren't obviously the same entity are actually the same entity ... and how you would go about discovering and proving and declaring that ...
Hypermedia (HATEOAS, REST, etc) is graph thinking as well. “endpoints” is not synonymous with “REST”, it is merely an address (likely paired with an expectation (or affordance).
A comprehensive framework for defining agent behavioral contracts (Abstract Agent Types) and managing their runtime interactions through Context Graphs, with full support for decentralized identity, verifiable credentials, causal reasoning, and provenance tracking.