Re: K3D TPAC Presentation - Vocabulary Slide for Demo A

Thank you  Daniel
I can see what the problem is
You havent got a clue :-)
But you are willing to lern right?
and you have a powerful machine at your fingertips right?

You have to enrol in one of my courses... or do self study
Let me point you in the right direction, but please note,
I do charge tuition fees

For today if you like, please give a ten minute overview

provide a state of the art review of 'spatial ontology' including
1. relevant publications, for example
http://www.fb10.uni-bremen.de/anglistik/langpro/webspace/jb/repository/downloads/del2.pd
2. a list of open vocabularies in the spatial knowledge domain
3. a list of VALID use cases
4. how does your architecture fit in the picture of existing spatial KR,


I am sorry I cannot allocate agenda time to present a vocab which clearly
showing... that you are on another planet
(note: everyone here is another planet as well)

but the demo..... would be okay..... what do you think.... provided it
shows some
capability  related to KR and Spatial domain

I thin, you can have one minute, if the demo is interesting we can spend
more time on it
If it does something else, we ll simpty move on to another topic *lots to
discuss

I thank you Daniel, for helping us to figure out that we have a lot of work
to be done


On Fri, Nov 14, 2025 at 11:46 AM Daniel Ramos <capitain_jack@yahoo.com>
wrote:

> Paola,
>
> Thank you for the detailed feedback and for clarifying what you need
> (again) in terms of scope. I’ll respond point‑by‑point so we can align (if
> possible) before TPAC.
>
> 1. Where K3D fits in your AI‑KR diagram
>
> In your “AI KR vocabularies / subdomains” figure, the K3D vocabulary I’m
> proposing for this CG belongs primarily in the DOMAIN ONTOLOGIES / ODD
> ellipse:
>
> K3D, for the purposes of this CG, is a domain ontology for spatial
> knowledge environments: Houses, Rooms, Doors, Nodes, Galaxy, Tablet, etc.,
> i.e. how AI and humans inhabit and navigate knowledge as 3D spaces.
> Each K3D Node also carries embeddings, so the same vocabulary naturally
> connects to KR learning (your “Knowledge Representation Learning” ellipse),
> and SleepTime / sovereign constraints relate to reliability engineering.
> But I agree that what is in‑scope here is the vocabulary for the spatial
> domain, not the GPU architecture or training methodology.
> So: in your diagram, K3D’s terms sit in DOMAIN ONTOLOGIES / ODD, with
> edges to KR learning and reliability, but I’m only proposing to standardize
> the domain vocabulary in this CG.
>
> 2. What the K3D spatial vocabulary actually is (with use cases)
>
> To address your concern that the PPT terms “do not seem to represent the
> spatial domain in relation to AI”, here are the core terms and a concrete
> use case:
>
> House: A persistent 3D knowledge environment (e.g. “Web Standards House”)
> containing rooms, shelves, and doors. Use case: a House that stores all
> AI‑KR artifacts (ontologies, vocabularies, test cases) as 3D rooms instead
> of a flat wiki.
> Room: A sub‑domain within a House (e.g. “AI KR Vocabularies Room”,
> “Reliability Engineering Room”), used to cluster related concepts spatially.
> Node: The atomic knowledge unit (a 3D object) that represents a concept
> (e.g. “ODD”, “Matryoshka Embedding”, “Model Card”). Each Node has:
> a URI / identifier,
> optional RDF metadata, and
> one or more embeddings (linking it to KR learning).
> Galaxy: The active embedding space (RAM) where those Nodes are laid out so
> that semantic proximity = spatial proximity (this is the KR learning side).
> Door: A typed portal linking Rooms or Houses and, optionally, external
> services (e.g. a Door from the “AI KR” House to an “Accessibility” House,
> or to a spec hosted elsewhere).
> Memory Tablet: The interface an agent uses to query and update the
> House/Galaxy (search, retrieve, log changes). In KR terms, it’s the access
> mechanism for vocabularies + metadata.
> Example use case:
> A W3C “AI KR House” where:
>
> The Domain Ontology / ODD part is modeled as Rooms and Nodes (e.g. a Room
> for “ODD vocabularies” containing Nodes for each term, linked to their
> formal definitions and examples).
> KR learning is represented by the Galaxy layout of those Nodes (e.g. how
> ODD vocab terms cluster with upper ontologies and reliability concepts).
> Reliability engineering Nodes live in a specific Room (e.g. “Reliability
> Engineering Room”), connected via Doors to the vocab Rooms.
> This is the domain I am trying to represent: spatial knowledge
> environments for AI systems and humans, not just the implementation details.
>
> 3. Clarifying the demo you requested
>
> In a previous exchange about the “AI‑Driven Web Standards Generator”
> session, you asked if I could demonstrate the MVCIC methodology without API
> keys, to show how multiple AI assistants can help draft standards.
>
> What I had in mind, concretely and within your constraints, is:
>
> A very small, browser‑based MVCIC demo where a human orchestrator and a
> small set of AI assistants (which can be local/open or pre‑computed)
> co‑draft a mini vocabulary and then use it to annotate a short example text.
> The focus is entirely on KR: how terms are proposed, refined, agreed upon,
> and then applied to real artifacts (e.g. labeling a short AI‑KR use case
> with the spatial vocabulary above).
> The “external validity” is that any CG member could reproduce the workflow
> with their own vocabularies, using the same step‑by‑step methodology, even
> with different tools.
> If this still feels out of scope for TPAC, I’m happy to postpone the demo
> and focus only on the vocabulary contribution for now.
>
> 4. On time and context
>
> I share your concern that TPAC time is extremely valuable. I am also
> paying all costs personally as an independent, self‑employed engineer: I
> live in Cidade Estrutural (a favela near Brasília), I am a registered
> electrical engineer (CREA‑DF) and run a small IT support business. The GPU
> hardware, AI API usage, and the entire K3D research and development were
> funded from my own pocket over the last year.
>
> I fully respect everyone’s time and attention, and I have been trying
> since early November to shape the K3D contribution to match the CG’s KR
> mission: short, vocabulary‑focused, with clear standards relevance. If
> markdown files are difficult to open, I can move the key content into a
> single PDF or directly into the body of an email/slide, with the same
> structure you requested: term → definition → use case → KR subdomain (e.g.
> ODD vs KR learning).
>
> If it helps, I can send you a one‑page table listing:
>
> each K3D term,
> its place in your diagram (ODD / KR learning / reliability), and
> a concrete use case and definition.
> Would that be a helpful next step?
>
> Best regards,
> Daniel
> On 11/14/25 12:14 AM, Paola Di Maio wrote:
>
> Thank you for sharing Daniel
>
> As you may know, TPAC time is very valuable for attendees
> So we need to make sure that even 15 minutes are spent appropriately
> Every minute in fact counts
>
> At this stage after a few exchanges with you I am not sure that what you
> are offering has anything to do with what we are doing here yet
> *not until you can scope it better or until we can understand it
>
> I do not mean to say that your architecture is not valuable, I am just
> saying
> that the exchanges that we have had so far have not  been meaningful ,
> although they are well formed
> This is the case of syntax  without real semantics *well formed but
> meaningless without context or with context
> that may not be logically sound.   But hay, who am I.
>
> I am concerned that you are maybe not clear yourself as to what  knowledge
> representation is
> nor how to pitch our work to this group
> But do not worry, you are not alone in that. Looks like a great deal of
> people, including senior experts
> still do not know what KR is, and this is our mission here
>
> Most people cannot open .md files, so I cannot tell what the .md file
> contains
> regarding the terms in the pptx file, they do not seem to represent the
> spatial
> domain in relation to AI
> If they do, this is what you must explain
>
> I asked a question: would the 3KD terms fit in the ODD space? *see the
> diagram below
> if so, please explain how so,  if not, then explain where does it belong?
> [image: Copy of AI KR VOCABS 3DK INTERSECT(3).jpg]
>
> If the vocabulary you are offering in the ppt is representing the spatial
> domain knowledge
> please give use case study. Explain what each term represents, how it is
> used, how it was derived etc
>
> if the vocabulary represents the architecture itself, or something else,
> then this is not in scope of this CG, simply because nobody understand
> what 3KD is het nor how it relates here
>
> As for the demo, you offered a demo showing how multiple llms can be
> orchestrated
>  to answer a query without API key,
> I am not sure what you have in mind!   Please ensure that whatever you
> want to demonstrate
> has external validity *can be generalized and made relevant to real world
> use cases
> and of course it must be related to KR
>
> If you intend to contribute vocabulary terms, please make sure they are
> clearly defining the knowledge domain you are representing
> with use cases, and if you plan to demo some useful novel LLM capability,
> that would be great but make sure
> you can explain it
>
> Either way, it is great that you feel inclined to show up and show your kit
> hopefully by continually refining your own thought process you ll produce
> the few drops of nectar
> that we may include in our overall distillate here, when we get to it
>
> This is once again, something that we all must do, and have been doing
> over the course of years and decades even
>
> PDM
>
> On Fri, Nov 14, 2025 at 1:00 AM Daniel Ramos <capitain_jack@yahoo.com>
> wrote:
>
>> Dear Paola and AI KR CG members,
>>
>> As requested, please find attached the vocabulary slide for tomorrow's
>> Demo A presentation (13 min + 2 min Q&A).
>>
>> The slide includes the 10 key terms that will be demonstrated through the
>> browser-based MVCIC methodology.
>>
>> Looking forward to tomorrow's presentation and discussion.
>>
>> Best regards,
>> Daniel Caldeira
>> EchoSystems AI Studios
>> Knowledge 3D (K3D) Project
>>
>