A Common Representation for Cross-Linguistic Meaning: Computational Architecture for Sub-Graph Matching Search on Ultrametric Semantic Trees
Status: Active — Building on published foundations
Program: Ultrametricity
Started: 2026-05-22
Directory: projects/nested-semantic-graph/
All human languages, regardless of morphological type, encode meaning as nested hierarchies of conceptual primitives connected by scope/modification relationships. This structure is ultrametric. The differences between languages are differences in linearization — how the tree is flattened into a temporal sequence and where chunk boundaries fall. The tree itself is invariant. This project develops the computational architecture for building search and AI systems that operate on these nested semantic graphs rather than flat token sequences, extending the conceptual foundations established in the 2026-05 ultrametric-language publication cluster.
This project is a computational implementation sequel to a body of recently published work (2026-05). The core conceptual argument — that language is an ultrametric tree — has been established. This project focuses on the engineering: how to build search, ranking, and matching systems on that tree.
DOI: 10.5281/zenodo.20328374 Role: The core conceptual paper. Establishes that polysynthetic languages demand nested ultrametric trees as the natural representation, that English-centric tokenization systematically fails, and that the digital infrastructure's "one word = one concept" assumption is parochial. The present project extends this from argument to architecture.
DOI: 10.5281/zenodo.20137616 | GitHub: github.com/rwnq8/language-info-architecture Role: Cross-linguistic Bayesian pipeline establishing the entropy gradient across morphological types (isolating: 6.48 bits/word → polysynthetic: 6.80 bits/word) and the mutual exclusion principle of mandatory feature clusters. Provides the quantitative grounding for the claim that languages differ systematically in information architecture.
DOI: 10.5281/zenodo.20287742 | GitHub: github.com/QNFO/Q-PNA Role: The actual neural network architecture implementing ultrametric geometry on Bruhat-Tits trees with syntactic token calculus for formal verifiability. The sub-graph matching search described here is the retrieval/inference complement to Q-PNA's encoding/representation.
| Paper | DOI | Role |
|---|---|---|
| The Tree at the Bottom of Thought | 10.5281/zenodo.20329583 | Synthesis of ultrametric branching across physics, math, linguistics, cognition |
| The Tree Is Real | 10.5281/zenodo.20325850 | Computational validation: 649 triples from biology/linguistics/physics, all ultrametric |
| Convergence, Consilience | 10.5281/zenodo.20302276 | Meta-analysis: convergence and consilience as signatures of hierarchical reality |
| Ultrametric Geometry as Common Structure | 10.5281/zenodo.20265907 | Cross-domain: ultrametric trees as common structure across 5 domains including cognition |
| Tree Distance Cophenetic | 10.5281/zenodo.20213043 | Mathematical formalization of cophenetic distance as unified hierarchical ontology |
| How Geometry Creates Memory | 10.5281/zenodo.20061155 | Threshold Principle: ultrametric distance creates containment — the geometric basis for fault tolerance |
| TREE OF FREQUENCIES | 10.5281/zenodo.20049051 | The physical/computational tree — frequency as universal coordinate, tree as fundamental geometry |
| Symmetry as a Grammatical Function | 10.5281/zenodo.20089746 | Symmetry emerging from grammatical constraints — the deep connection between grammar and geometry |
| Repository | Purpose |
|---|---|
| github.com/rwnq8/ultrametric-ai-poc | Working proof-of-concept for ultrametric AI |
| github.com/rwnq8/language-info-architecture | Language information architecture pipeline |
| github.com/rwnq8/quantum-laws-of-form | Laws of Form / distinction calculus implementation |
| github.com/rwnq8/verb-lexicon | Verb lexicon for semantic parsing |
| github.com/QNFO/Q-PNA | Q-PNA neural architecture |
| Project | Location | Relevance |
|---|---|---|
| PILE OF BABEL | Archive\projects\2025\10\PILE OF BABEL\ |
Same Rosetta Stone architecture: common representation beneath diverse surface forms. "Terminology Crosswalk" and "Crosswalk Mandate" are the operational principles. |
| Semantic Observatory | Archive\projects\2025\09\Semantic Observatory\ |
"Semantic field" concept, 5-layer stack architecture |
| Grammar of Interaction | Archive\projects\2025\09\Grammar of Interaction\ |
Graph formalism (directed acyclic hypergraphs) with "grammar" metaphors |
Nested Semantic Graph (this project)
— Computational Search Architecture —
│
┌────────────────┼────────────────┐
│ │ │
Linguistics Ultrametric Computation
(grounded in Topology (building on
Few Become (grounded in Q-PNA spec,
One, Lang- Tree Cophenetic, ultrametric-
Info-Arch) How Geometry ai-poc)
Creates Memory)
│ │ │
▼ ▼ ▼
Cross-linguistic Sub-graph Python prototypes:
semantic parsing matching with parser, encoder,
(S3) ultrametric matcher, ranking
ranking (S4) engine (P1 tasks)
# Step 1: Set up environment (TBD)
# Step 2: Run examples| File | Purpose |
|---|---|
0.1.md |
First versioned draft — formalization of sub-graph matching search |
0.1.py |
Python prototype: ultrametric distance on example semantic trees |
0.2.py |
Python prototype: brute-force subgraph matcher |
All references above with DOIs are published and accessible. External frameworks referenced:
- Abstract Meaning Representation (AMR) — Graph-based semantic representation (Banarescu et al., 2013)
- Universal Dependencies (UD) — Cross-linguistic dependency annotation
- Syntactic Token Calculus — Referenced in Q-PNA spec and Few Become One
Last updated: 2026-05-22