Library Taxonomy Retrieval

Dual retrieval: embedding similarity PLUS taxonomic adjacency in task ontology. Use when related concepts use different wording. Scope boundary: triangulation for facts → `survey-triangulation`.

CatCorner22 Updated

File contents

Library taxonomy retrieval

#24 · Domain: Library science · Category: memory · Difficulty: 🟡 Medium

Core principle

Classification maps relationships — not just keyword proximity.

AI problem addressed

Embedding-only misses structurally related but differently worded concepts.

Implementation

  1. Semantic search (embeddings)
  2. Taxonomic browse (adjacent ontology nodes) Merge results; dedupe; rank by task relevance.

Boundaries

  • Prototype as prompt scaffold (🟢) before full pipeline middleware (🟡/🔴)
  • Category router: ai-transfer-memory
  • Catalog: ai-transfer-ecosystem-primer
  • Runtime plugin id: catalog_retrieval
  • Also implements: memory_palace
  • Merge notes: Also absorbs memory_palace: spatial room-walk as one retrieval mode.

CatCorner22/Cursor_Skills/tree/main/skills/ai-transfer/library-taxonomy-retrieval commit 5e5dbc5a85

Frequently asked questions

npx skillmds@latest add catcorner22/library-taxonomy-retrieval