KB completeness has a formal toolkit beyond obligatory attributes — cardinality assertions, the No-Change Assumption, and text-extraction recall

knowledge-graphcompletenesscardinalitygap-detectionPKM

The same survey that grounds claim-obligatory-attributes-as-gap-signal offers three further completeness instruments, each mappable to vault practice:

1. Cardinality assertions — comparing a relation's recorded cardinality against its real one is the formal primitive of completeness measurement. (2026-09-11 audit — the paper's words: "Cardinality assertions can be used to infer recall for a given selection condition by computing the division from Definition 3, |σ(K)|/n. Moreover, in the special case where |σ(K)| = n, they can be used to infer completeness" and "If we know that the KB is complete, we can infer that the true cardinality of PhD students she advised is 7" (§4). The sentence this note formerly carried as a quotation was a paraphrase; see source_quote_note.) Vault analogue: "this cluster should have N members" claims (a MOC that names five clusters but links four is measurably incomplete).
2. No-Change Assumption (NCA) — if repeated observations stop adding new facts, treat the region as converged: a lightweight temporal completeness signal. (2026-09-11 audit — verbatim, §3.2: "the No-Change Assumption says that if the number of objects has not changed over recent editions of the KB, then it has 'converged' to the true number, and no more objects are missing." The abbreviation NCA is the vault's; the paper does not use it.) Vault analogue: a topic whose captures stop yielding new claims has stopped growing — the loop-until-dry intuition, formalized.
3. Text-extraction-based recall — compare what a source document mentions against what the KB extracted from it. Vault analogue: the promotion accounting's not_promoted lists exist precisely so recall against a capture is auditable.

One quantitative boundary from the paper stays flagged per the capture: the LM-KBC 2023 best-system figure (69% F1 on cardinality prediction) is cited within the survey from shared-task proceedings — [unverified-quant — needs primary] until read there. See question-gap-detection, moc-machine-self-knowledge.