GOURMET Knowledge Gap Analysis & Filling Strategy

Date: 2026-06-09

Current State

Main Knowledge Graph (gourmet_kg.db — kanban board)

• 9,665 entities ✅ (rich data)
• 783 observations ✅
• 11 domains ✅
• 6 symbols ✅
• 2 bridges ✅
• 14,504 edges ✅
• But: 0 embedding vectors ❌

Gematria KG (gematria_kg.db — vault)

• 1 entity, 0 relationships, 0 embeddings ❌ (essentially empty)
• This is the database referenced by research converges analysis

Operational DB (gourmet.db — root)

• 137 domain observations ✅
• 68 causal signals ✅
• 6 domains (Political, Military, Religious, Economic, Bocurrency, DeepDive) ✅
• But: 0 entity embeddings ❌

The Gap

The main KG has 9,665 entities with rich properties (gematria values, digital roots, domains, multiple gematria systems) but no embedding vectors. The convergence analysis (crystallization #4: KG embeddings mirror theoretical predictions) cannot be verified without embeddings.

Filling Strategy (v30.2)

Phase 1: Embedding Generation

1. Extract all 9,665 entities with their properties from gourmet_kg.db
2. For each entity, create a text representation combining:
   • Entity label/names
   • Domain assignments
   • Gematria values (simple, english, jewish, pythagorean)
   • Relationship context (top 5 edges)
3. Generate embedding vectors using a sentence transformer (e.g., all-MiniLM-L6-v2 via sentence-transformers)
4. Store embeddings in gematria_kg.db

Phase 2: Migration

1. Copy all entities from gourmet_kg.db → gematria_kg.db
2. Preserve all properties as JSON
3. Add embedding vectors
4. Create proper indexes for similarity search

Phase 3: Convergence Analysis

1. Compute pairwise cosine similarity between cross-domain entity pairs
2. Identify clusters that span multiple domains
3. Compare clusters against theoretical predictions (crystallization #4)
4. Document results in vault report

Priority

This is Stream 2 of v30 (t_87dbfde0). Depends on v30.1 (oracle grounding) only for real signal data to validate embeddings against. Can run in parallel.