Re: Reification

Adam, semantic‑web and AI‑KR folks,

Thank you for sharing the schema.org discussion and the JSON‑LD‑star 
example for “article about whether an Action adheresTo Rules.” That kind 
of reified statement is exactly the sort of structure I want to track 
explicitly in my work on spatial KR.

Concretely, in K3D we’ve been treating:

Actions, rules and statements as Nodes in a 3D House/Galaxy;
Relations like adheresTo / violates / discusses as rays and edges;
and time/adequacy as fields attached to those structures (e.g., when, by 
whom, with what confidence).
On the implementation side, we’re currently experimenting with a small 
extension to our GPU‑native RPN engine to support three‑valued logic and 
balanced ternary signals on PTX, e.g.:

adheres / violates / unknown,
consistent / contradictory / undecided,
attract / neutral / repel in semantic fields.
Those ternary values are intended to complement, not replace, richer KR 
formalisms: they sit alongside embeddings and symbolic structures and 
can be computed very efficiently on GPU. I see a lot of potential in 
combining:

patterns like your JSON‑LD‑star reification (Action + Rule + about 
relationships),
plausibility layers like Dave’s Plausible Knowledge Notation (PKN),
and these lightweight ternary fields for “how well does this action 
adhere to this rule, right now, in this domain?”
There is also a resource‑ and carbon‑angle here that Milton has been 
highlighting from the AI‑for‑Good / ICT4D side. Our design is explicitly 
aimed at:

small, recursive models rather than ever‑growing LLMs;
procedural compression and GPU‑local memory instead of giant host‑RAM 
arrays;
running serious KR + reasoning on mid‑range hardware rather than 
hyperscale datacenters.
If we want reified KR about actions, rules and impacts to be used in 
SIDS, indigenous communities, and low‑resource contexts, this question 
of representation + efficiency becomes very concrete.

If it’s helpful for the discussion, here are two implementation‑oriented 
notes from my side (not standards proposals, just working docs):

Spatial encoding of domains, concepts and relations (stars, rays, 
Garden, Museum):

https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/SPATIAL_KR_VISUAL_ENCODING.md 
<https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/SPATIAL_KR_VISUAL_ENCODING.md> 


Chain file for exploring ternary RPN semantics over PTX (balanced 
ternary / three‑valued logic):

https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/RPN_TERNARY_SETUN_CHAIN.md 
<https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/RPN_TERNARY_SETUN_CHAIN.md>

Projection of the carbon print impact in 10 years (If the industry 
adopts K3D):

https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/CARBON_BLUEPRINT_10_YEAR_PROJECTION.md 


I’d be very interested in seeing how a pattern like the one you showed 
(Action adheresTo Rule, reified and “about”‑ed by articles) could be 
treated as a first‑class KR pattern (RDF/JSON‑LD‑star + PKN‑style 
plausibility) and also as a spatial substrate that AIs and humans can 
actually navigate, introspect, and update over time.

Best regards,
Daniel