Re: reminding the scope for this AI KR CG

Paola, all,

Thank you for sharing the updated diagram and the clarification on where 
you now see the focus of the AI‑KR CG.

As I understand your updated diagram, the “in scope” areas are:

KR languages / formalisms and upper / foundational ontologies,
vocabularies and concept maps around “Web AI standards”,
knowledge representation learning, and
reliability engineering for AI systems,
with domain‑specific KR / “domain ontologies, ODD” treated as out of 
scope for this group.

I’m happy to respect that boundary and keep detailed discussion of 
concrete domain Houses and application‑specific ontologies outside this 
list.

At the same time, it may be useful for the group to know that the work 
I’ve been sharing under the name Knowledge3D (K3D) is not just about 
domain Houses. It also lives squarely in several of the blue “in‑scope” 
bubbles in your diagram:

KR languages / formalisms and upper‑level structures
K3D defines a structural vocabulary for multi‑modal KR (Houses, Rooms, 
Nodes, Doors, Galaxy, Tablet) that is independent of any one domain and 
substrate. The intent is to provide a shared “spatial KR language” into 
which different domains of discourse can be embedded. This is documented 
here:

Spatial KR visual encoding (domains, concepts, relations, time):
https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/SPATIAL_KR_VISUAL_ENCODING.md
Vocabulary specifications:
https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/vocabulary
Knowledge representation learning
K3D implements an explicit KR‑learning pipeline (Galaxy ↔ House 
consolidation, ternary RPN reasoning, procedural compression) rather 
than treating knowledge as an undifferentiated vector space. That sits 
very close to what Dave called out in his Plausible Knowledge Notation 
(PKN) note: reasoning over imperfect, contextual knowledge with explicit 
structure. Some relevant technical notes are:

RPN ternary / three‑valued logic and domains of discourse:
https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/RPN_TERNARY_SETUN_CHAIN.md
Overall architecture whitepaper:
https://github.com/danielcamposramos/Knowledge3D/blob/main/K3D_Technical_White_Paper.md
Reliability and adequacy
The architecture is designed around bounded domains, adequacy rather 
than “scaling will fix it”, and explainable spatial traces (for both 
humans and machines). There is also an explicit energy / carbon angle, 
which Milton and others have highlighted as important for AI‑for‑Good:

Carbon blueprint for a 10‑year horizon if K3D‑style architectures are 
adopted:
https://github.com/danielcamposramos/Knowledge3D/blob/main/docs/CARBON_BLUEPRINT_10_YEAR_PROJECTION.md
On the natural language side: my intent is not to replace formal KR work 
with a numeric trick, but to provide a substrate where natural‑language 
statements (including PKN‑style plausible knowledge and reified 
JSON‑LD/RDF patterns like the “Action adheresTo Rule” example Adam 
shared) can be represented, learned over, and inspected spatially. 
Milton’s emphasis on domains of discourse and linguistic plurality maps 
directly to how K3D treats each House.

Given your updated scope, I propose the following as a way to avoid 
noise on the list:

I will keep domain‑specific Houses and application ontologies (e.g., 
BIM, disaster response, etc.) off this list unless explicitly requested.
When I do share K3D material here, I will focus strictly on the parts 
that overlap with your blue bubbles:
KR vocabularies and structural patterns,
KR‑learning and plausibility layers,
and reliability / adequacy considerations that may be relevant to Web AI 
standards work.
If that still doesn’t fit what you now want AI‑KR to do, I’m happy to 
treat K3D as an external case study that people can look at (or ignore) 
as they wish, and I’ll focus on other venues for the rest.

In any case, I appreciate the clarification and the thoughtful 
contributions from Dave, Milton and others on this list. My aim is 
simply to contribute one concrete implementation example to the broader 
discussion on AI‑KR, within whatever boundaries the CG agrees are 
appropriate.

Best regards,
Daniel