Vector search
Retrieval by similarity in an embedding space, returning items whose vector representation is closest to a query's.
Why it matters
Answers 'what looks like this question.' Finance more often needs 'what is connected to this entity, and how' — an exposure chain, a constituent history, a data lineage — which is a traversal, not a similarity search. No amount of embedding quality converts one into the other. A further governance problem: reconstructing a past answer requires the index as it stood at the time, which most deployments cannot produce.
What it is not
These are routinely confused with Vector search. The distinctions are not pedantic — each one has consequences for how a system is governed.
Similarity finds things that resemble each other. Traversal follows declared relationships. Different questions, different answers.
Relationships
Typed edges into the rest of the ontology. These are what make the canon traversable rather than merely readable.
| Verb | Target | Meaning |
|---|---|---|
notEquivalentTo | Graph database | The two are routinely conflated and are distinct. |
relatedTo | Graphrag | An association too weak or too general for a stronger verb. |
Record
| Canonical identifier | QIS-TERM-00045 |
| Status | Canonical industry term |
| Adoption | Widely used |
| Domain · Layer | Stack categories · Intelligence |
| Origin | Approximate nearest-neighbor literature; productized from 2021. |
| Semantic aliases | None recorded. |
| First published | 2026-08-02 |
| Last reviewed | 2026-08-02 · 180-day cycle |
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