- From: Daniel Ramos <daniel@echosystems.ai>
- Date: Thu, 11 Dec 2025 22:47:13 -0300
- To: danbri@gmail.com
- Cc: "public-aikr@w3.org" <public-aikr@w3.org>, semantic-web@w3.org, public-cogai@w3.org, public-s-agent-comm@w3.org, Dave Raggett <dsr@w3.org>, timbl@w3.org, torvalds@linux-foundation.org, Milton Ponson <rwiciamsd@gmail.com>
- Message-ID: <da4ca13f-ddd1-4e11-9baf-7bb437c2b408@echosystems.ai>
Dan, all,
Fair point—here’s the executive version instead of the full Claude artifact:
* What: K3D = spatial KR + glTF extension for embeddings + local‑first
reasoning on raw PTX (no ML frameworks). It treats 3D
position/structure as the semantic substrate, so agents/humans share
the same objects and traces.
* Why: A third path between Semantic Web’s annotation burden and
centralized LLM extraction—sovereign cognition without shipping data
out or relying on centralized training.
* Standards fit: JSON‑LD/WebXR/WebGPU on the W3C side; glTF extension
path on the Khronos side. Directly aligns with AIKR scope (KR
languages/learning/reliability) and useful for WebAgents/agent comm
work (stateful KR bridge).
* Demos/refs: Repo with specs and TPAC demo:
https://github.com/danielcamposramos/Knowledge3D (see
docs/vocabulary/
<https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/vocabulary>
and docs/w3c_tpac_2025/
<https://github.com/danielcamposramos/Knowledge3D/tree/main/docs/w3c_tpac_2025>).
Happy to provide a one‑pager or a quick call if that helps more than
longform. And I’ll keep future posts to concise summaries.
Daniel
On 12/11/25 4:53 PM, Dan Brickley wrote:
> Next time, maybe just send the prompt
>
> On Thu, Dec 11, 2025 at 17:29 Daniel Ramos <daniel@echosystems.ai> wrote:
>
> Claude.ai generated. Access in full here:
>
> https://claude.ai/public/artifacts/0f8e078a-dd13-473d-b419-03f56e4d224b
>
>
>
> 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>
>
Received on Friday, 12 December 2025 01:47:27 UTC