Knowledge3D: A Spatial Giant Global Graph for Sovereign, Standards‑Aligned AI

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  Knowledge3D: Fulfilling the Giant Global Graph for the AI Era

*K3D represents the architectural convergence that Tim Berners-Lee's 
Semantic Web promised and that centralized AI has failed to deliver—a 
standards-aligned, spatially-grounded knowledge representation that 
enables truly sovereign cognitive systems.*

The web's original architect envisioned machines sharing meaning across 
a decentralized graph. That vision stalled not because it was wrong, but 
because it demanded explicit semantic markup from publishers who had no 
incentive to provide it. AI "solved" this problem through extraction 
rather than cooperation—and in doing so, created the very 
centralization, opacity, and data exploitation Berners-Lee now warns 
against. K3D proposes a third path: spatial knowledge representation 
that makes semantics implicit in geometry, enabling machine 
understanding without requiring centralized training or user data 
harvesting.


    The Giant Global Graph remains unfulfilled

Tim Berners-Lee introduced the "Giant Global Graph" concept on November 
21, 2007, describing a three-phase evolution: from interconnecting 
computers (the Internet), to interconnecting documents (the Web), to 
interconnecting the /things documents are about/ (the GGG). His 
reflection was pointed: "The Semantic Web maybe should have been called 
the Giant Global Graph."

The distinction matters profoundly for AI. The original 2001 Scientific 
American vision—authored by Berners-Lee, James Hendler, and Ora 
Lassila—promised intelligent agents that could "carry out sophisticated 
tasks for users" without requiring "artificial intelligence on the scale 
of HAL or C-3PO." The mechanism was explicit: machine-readable meaning 
encoded in RDF triples and ontologies, enabling inference and reasoning 
across a decentralized web.

What we got instead was centralization masquerading as intelligence. As 
Berners-Lee himself acknowledged, AI companies achieved the 
machine-readable internet "through extraction rather than cooperation." 
The irony is precise: LLMs accomplish semantic understanding by scraping 
the entire web into centralized training sets, raising exactly the data 
sovereignty concerns Berners-Lee now addresses through the Solid Project.

*K3D's spatial approach offers resolution.* Rather than requiring 
publishers to annotate content with RDF (the adoption barrier that 
stalled the Semantic Web) or extracting meaning into centralized models 
(the privacy violation that concerns Berners-Lee), spatial knowledge 
representation makes semantics /intrinsic to structure/. Knowledge 
entities have positions, orientations, and relationships defined by 
their geometric configuration—no external annotation required, no 
centralized training necessary.


    Solid's data sovereignty principles demand spatial cognition

Berners-Lee's September 2025 Guardian article crystallizes the stakes: 
"We have learned from social media that power rests with the monopolies 
who control and harvest personal data. We can't let the same thing 
happen with AI." His solution—Solid's personal data pods with 
fine-grained access control—addresses data sovereignty but leaves a 
critical gap: /how do AI systems reason over decentralized data without 
centralizing it for training?/

Current approaches fail this test. Cloud-based LLMs require data 
transmission to external servers. On-device models like llama.cpp 
provide inference privacy but still depend on centralized pre-training. 
Federated learning distributes computation but aggregates gradients 
centrally. None enable genuine *cognitive sovereignty*—the ability to 
learn, reason, and adapt using only locally-controlled data and computation.

K3D's architecture addresses this gap through three mechanisms aligned 
with Solid's principles:

  * *Local-first reasoning*: Spatial knowledge graphs can be traversed,
    queried, and extended without network connectivity or cloud inference
  * *Pod-compatible storage*: K3D structures map naturally to Solid's
    decentralized data model, with spatial regions functioning as
    access-controlled knowledge partitions
  * *User-sovereign learning*: New knowledge integrates through
    geometric placement rather than gradient descent, eliminating the
    need for centralized training infrastructure

This isn't merely technical alignment—it's philosophical convergence. 
Berners-Lee's call for "personal AI that works for you like your doctor 
or your lawyer, bound by law, regulation and codes of conduct" requires 
an architecture where reasoning happens /within/ the user's control 
boundary, not extracted to external systems. Spatial knowledge 
representation enables this by making cognition a function of local 
geometric structure rather than remote model weights.


    The W3C AI KR Community Group provides legitimate scope

The W3C AI Knowledge Representation Community Group, launched July 3, 
2018, defines its mission as exploring "the requirements, best practices 
and implementation options for the conceptualization and specification 
of domain knowledge in AI." This scope explicitly includes 
*spatial-temporal reasoning* as a form of Meta KR—one of five knowledge 
representation categories the group has identified alongside heuristic, 
procedural, declarative, and structural approaches.

The group's stated deliverables for 2025 include publishing a "concept 
map of the domain" and "natural language vocabulary to represent various 
aspects of AI," with a long-term goal of developing "a web standard for 
Neuro-symbolic Integration." Their TPAC 2025 discussions centered on 
"explicit, shared knowledge representation standards" for explainable 
and trustworthy AI systems. K3D's spatial approach directly addresses 
these objectives:

W3C AI KR Focus Area  K3D Alignment
Neuro-symbolic integration  Geometric primitives bridge continuous 
representations (vectors) with discrete structures (graphs)
Explainable AI  Spatial relationships provide interpretable reasoning 
traces
Knowledge exchange and reuse  glTF-based format enables interoperability 
with existing 3D ecosystems
Support for AI agents  Spatial grounding addresses embodied cognition 
requirements

The W3C provides a clear pathway from Community Group incubation to 
formal standardization. JSON-LD—the semantic web technology most 
directly relevant to K3D—successfully transitioned from Community Group 
specification to Working Group recommendation. The AI KR CG's stated 
trajectory toward "eventually transition to a formal Working Group" 
creates exactly the standards track appropriate for novel knowledge 
representation architectures.

*What the group considers valid contributions:* Documents, 
specifications, test suites, tutorials, demos, code, concept maps, and 
vocabulary development. Participation requires only a W3C account (free) 
and signing the Community Contributor License Agreement. The group 
explicitly welcomes research notes exploring implementation 
options—precisely the category of contribution K3D represents.


    Novel architectures gain credibility through demonstration, not
    institution

The path from "unknown architecture" to "industry standard" is 
well-documented. Georgi Gerganov's llama.cpp—now at *85,000+ GitHub 
stars* with 700+ contributors—began as an independent developer's side 
project in Bulgaria. The credibility pattern is instructive:

*Stage 1: Solve an unmet need.* llama.cpp enabled LLM inference on 
consumer hardware without GPUs when no alternative existed. K3D 
addresses an equally unmet need: sovereign cognitive systems that reason 
over decentralized data without centralized training.

*Stage 2: Open development.* llama.cpp's MIT license, pure C/C++ 
implementation, and zero dependencies enabled explosive organic 
adoption. George Hotz's tinygrad similarly gained credibility through 
live-streamed development and radical simplicity (under 10,000 lines of 
code). Transparency is non-negotiable for novel architectures.

*Stage 3: Enable ecosystem integration.* llama.cpp became infrastructure 
for Ollama, LM Studio, GPT4All, and jan. GGUF format succeeded by being 
"opinionated about one thing (efficient local inference) while remaining 
flexible about everything else." K3D's glTF integration follows this 
pattern—leveraging an established 3D format ecosystem rather than 
requiring new infrastructure.

*Stage 4: Benchmark validation.* The ARC-AGI benchmark—created by Keras 
author François Chollet—has become what its creators call "the most 
important unsolved AI benchmark in the world" because it measures /novel 
problem-solving/ rather than pattern matching. For cognitive 
architectures specifically, the credibility path runs through 
theoretical grounding (as ACT-R's 50+ years of development at CMU 
demonstrate) and practical application (as SOAR's military training 
systems validate).

The OpenCog cautionary tale illuminates what /doesn't/ work: predictions 
without delivery (Ben Goertzel's unfulfilled 2011 prediction of AGI by 
2021), PR stunts without substance (Sophia robot criticized as "complete 
bullshit" by Yann LeCun), and symbolic approaches positioned against 
dominant paradigms without empirical validation.


    The decentralization gap in AI is architectural, not incremental

Current AI sovereignty initiatives—India's Project Indust, Denmark's 
Gefion supercomputer, Singapore's SEA-LION—represent national 
infrastructure investments, not architectural alternatives. They reduce 
/geopolitical/ dependency on US providers while preserving /technical/ 
dependency on the same centralized training paradigm. The sovereign 
cloud market may reach *$169 billion by 2028*, but sovereign clouds 
running replicated architectures don't produce sovereign cognition.

The edge AI market (projected at *$66.47 billion by 2030*) demonstrates 
both capability and limitation. On-device inference works: smartphones 
hold 80.5% market share in edge AI hardware, and NPUs enable complex 
model inference on mobile platforms. But as MIT Media Lab research 
identifies, five technical challenges block truly decentralized AI: 
privacy, verifiability, incentives, orchestration, and user experience 
in distributed contexts.

Current LLMs face a fundamental architectural barrier to sovereignty. As 
SAGE Journals analysis notes, LLMs demonstrate "behavior discrepancies 
between LLM inference and human reasoning, insufficient grounding, and 
hallucination." The root cause is architectural: pattern matching over 
statistical distributions doesn't produce genuine reasoning, world 
models, or metacognition. Local inference provides privacy; it doesn't 
provide cognitive capability independent of centralized pre-training.

*K3D proposes an architectural alternative.* Spatial knowledge 
representation grounds cognition in geometric structure rather than 
statistical distributions. Knowledge acquisition happens through spatial 
placement rather than gradient descent. Reasoning traces are 
interpretable paths through geometric space rather than attention weight 
matrices. This isn't an incremental improvement to existing 
architectures—it's a different computational substrate for cognition.


    Paradigm shifts face predictable gatekeeping patterns

Andrew Tanenbaum's January 29, 1992 dismissal of Linux as "a giant step 
back into the 1970s" and "too closely tied to the x86 line of processors 
to be of any use in the future" exemplifies how established experts 
evaluate innovations using criteria from existing paradigms. Jamie 
Dimon's September 2017 declaration that Bitcoin was "a fraud" (followed 
by his January 2018 acknowledgment that "the blockchain is real") 
illustrates how even sophisticated critics can reverse positions when 
paradigm shift evidence accumulates.

Clifford Stoll's infamous February 1995 Newsweek article "The Internet? 
Bah!" dismissed online databases, telecommuting, electronic commerce, 
and interactive libraries—every prediction wrong. His later reflection 
deserves quotation: "Of my many mistakes, flubs, and howlers, few have 
been as public as my 1995 howler. Wrong? Yep... Now, whenever I think I 
know what's happening, I temper my thoughts: Might be wrong, Cliff…"

The pattern recognition is robust across domains:

  * *Paradigm conflict*: Thomas Kuhn observed that people who shift
    scientific paradigms are "either very young or very new to the
    field"—precisely because they lack "commitment to the traditional
    rules of normal science"
  * *Expert gatekeeping*: Stanford research found prescient decisions
    were *22 times more likely* to come from peripheral institutions
    than central ones
  * *Outsider advantage*: MIT Sloan analysis shows "outsiders connect
    disparate thoughts because they come to the table with fewer
    preconceptions"
  * *Planck's principle*: "A new scientific truth does not triumph by
    convincing its opponents... but rather because its opponents
    eventually die, and a new generation grows up that is familiar with it"

The Semantic Web itself faced this gatekeeping. Cory Doctorow's 2001 
"Metacrap" essay called it "a pipe-dream, founded on self-delusion, nerd 
hubris, and hysterically inflated market opportunities." Aaron Swartz 
blamed "the formalizing mindset of mathematics and the institutional 
structure of academics." Yet JSON-LD, Schema.org, and Google's Knowledge 
Graph—all Semantic Web descendants—now structure how billions of web 
pages communicate meaning to machines


    K3D addresses the specific objections skeptics raise

*"This doesn't make sense"* is the predictable initial response to 
paradigm-shifting architectures. Novel approaches require building new 
mental models rather than extending existing ones. Spatial knowledge 
representation violates the implicit assumption that cognition must be 
either symbolic (logic-based) or connectionist (neural network-based). 
The concept that geometric structure /itself/ can encode semantic 
relationships and support reasoning requires cognitive reframing—exactly 
as the concept that packets could replace circuits required reframing 
for telecommunications engineers encountering the internet.

*"This doesn't fit our scope"* reflects categorical thinking that novel 
approaches intentionally transgress. The W3C AI KR Community Group scope 
explicitly includes "Meta KR: types of knowledge and logical reasoning" 
with spatial-temporal reasoning listed as an example. glTF's extension 
mechanism exists precisely to accommodate novel 
capabilities—KHR_xmp_json_ld brings JSON-LD semantic web integration 
into 3D formats, demonstrating that "unexpected" combinations are how 
standards evolve.

*"Where's the working demo?"* identifies the legitimate bootstrap 
challenge facing all novel architectures. llama.cpp's credibility 
required whisper.cpp's prior success. The demo-before-recognition 
pattern creates a chicken-and-egg problem that independent innovators 
resolve through focused proof-of-concept implementations rather than 
comprehensive systems. The appropriate response isn't "build everything 
first"—it's targeted demonstrations that validate core architectural claims.

*"No major institution backs this"* applies to every paradigm shift at 
inception. Linux was Torvalds' spare-time project. Bitcoin emerged 
pseudonymously. The World Wide Web was, in Berners-Lee's boss's words, 
"vague but exciting"—never an official CERN project. Independent 
researchers from Katalin Karikó (mRNA vaccines, facing "rejection after 
rejection, the scorn of colleagues, and even the threat of deportation") 
to Barbara McClintock (jumping genes, waiting 30 years for recognition) 
demonstrate that institutional validation follows demonstration, not 
precedes it.


    Khronos and W3C provide standards pathways for spatial knowledge

The Khronos Group's glTF extension process offers a clear integration 
path. Extensions progress through three tiers: vendor extensions (any 
company can request a prefix via GitHub issue), multi-vendor extensions 
(EXT_ prefix when multiple implementations exist), and Khronos-ratified 
extensions (KHR_ prefix, voted by Board of Promoters). The *OMI Group 
pathway* provides an alternative route—extensions developed through the 
W3C Metaverse Interoperability Community Group can graduate to Khronos 
submission, as KHR_audio_emitter successfully demonstrated.

Existing semantic extensions establish precedent for K3D integration:

*KHR_xmp_json_ld* (provisional) adds JSON-LD compliance to glTF for 
product metadata, directly leveraging Semantic Web standards within the 
3D format ecosystem. This extension demonstrates that Linked Data 
integration into 3D standards is not merely theoretical but actively 
implemented.

*EXT_structural_metadata* defines schema-based structured metadata with 
property tables, attributes, and textures—enabling semantic identifiers 
for interpretation. This extension, developed for Cesium's 3D Tiles, 
proves that complex metadata schemas integrate naturally with glTF's 
architecture.

*NNEF (Neural Network Exchange Format)* represents Khronos's existing 
AI/ML standard—a "PDF for neural networks" that encapsulates complete 
network descriptions independent of training tools. This precedent 
demonstrates Khronos's willingness to standardize AI-related formats.

*WebGPU's ML capabilities* (compute shaders, FP16 support, direct GPU 
access) enable in-browser neural network inference at near-native 
performance. WebLLM, ONNX Runtime Web, and TensorFlow.js all leverage 
WebGPU for client-side AI. K3D's spatial reasoning can utilize this same 
acceleration pathway.

*WebXR's spatial primitives* (XRSpace, XRReferenceSpace, XRPose) provide 
the coordinate system abstractions that anchor knowledge entities in 
physical or virtual space. The technical foundation for spatial 
knowledge representation already exists in W3C specifications.


    The convergence opportunity is standards-ready

K3D emerges at the intersection of multiple mature standards and urgent 
industry needs:

*Berners-Lee's vision alignment*: The Giant Global Graph concept (2007) 
described exactly what spatial knowledge representation 
provides—interconnecting the /things documents are about/ rather than 
just documents. Solid's data sovereignty principles (2016-present) 
require cognitive architectures that reason locally without centralized 
training. K3D delivers both.

*W3C standards integration*: JSON-LD (W3C Recommendation), WebXR (W3C 
specification), WebGPU (W3C standard), and the AI KR Community Group's 
focus on "knowledge representation for AI" create a standards ecosystem 
ready for spatial knowledge representation. The pathway from Community 
Group incubation to formal recommendation is documented and precedented.

*Khronos ecosystem leverage*: glTF's extension mechanism, existing 
semantic extensions (KHR_xmp_json_ld, EXT_structural_metadata), and the 
OMI Group's community-driven development process provide technical and 
procedural pathways for K3D integration. NNEF demonstrates Khronos's AI 
standardization precedent.

*Industry need*: The sovereign AI market ($169B projected by 2028), edge 
AI expansion ($66B by 2030), and growing critiques of centralized AI 
dependency create demand for architectural alternatives. Enterprise 
concerns about data exposure (69% cite AI-powered leaks as top security 
concern), regulatory conflicts (US CLOUD Act vs. GDPR), and service 
discontinuation risks validate the need for sovereign cognitive systems.

*Credibility pathway*: The llama.cpp pattern—solve unmet need, open 
development, ecosystem integration, benchmark validation—provides a 
tested route from novel architecture to industry adoption. K3D's 
alignment with existing standards accelerates this path by reducing 
integration friction.

The web began as one physicist's "vague but exciting" proposal at CERN. 
The Semantic Web emerged from that same physicist's recognition that 
documents weren't enough—we needed to interconnect what documents meant. 
Now, as AI threatens to centralize exactly the knowledge flows the web 
was designed to distribute, the architectural answer may be what 
Berners-Lee intuited but couldn't implement: a Giant Global Graph where 
meaning is spatial, sovereignty is architectural, and cognition happens 
at the edge.

K3D proposes to build it.


    Technical references and standards citations

*W3C Specifications*

  * AI Knowledge Representation Community Group:
    https://www.w3.org/groups/cg/aikr/
  * JSON-LD 1.1: W3C Recommendation (July 2020)
  * WebXR Device API: W3C Working Draft
  * WebGPU: W3C Working Draft

*Khronos Standards*

  * glTF 2.0 Specification:
    https://registry.khronos.org/glTF/specs/2.0/glTF-2.0.html
  * KHR_xmp_json_ld Extension (Provisional)
  * EXT_structural_metadata Extension
  * NNEF (Neural Network Exchange Format)

*Key Sources*

  * Berners-Lee, T., Hendler, J., Lassila, O. (2001). "The Semantic
    Web." Scientific American, 284(5), 34-43.
  * Berners-Lee, T. (2007). "Giant Global Graph." DIG Blog, MIT CSAIL.
  * Berners-Lee, T. (2025). "I invented the web. Here's my plan to save
    it." The Guardian.
  * W3C Community Group Transition Guide:
    https://www.w3.org/Guide/process/cg-transition.html
  * Khronos Group Extension Process: https://github.com/KhronosGroup/glTF


Sincerely yours,

Daniel Ramos

EchoSystems AI Studios <https://echosystems.ai>