Knowledge-gap detection — how a system finds what it doesn't know
The vault researched how to detect its own ignorance — and the notes that came back split cleanly into two families by substrate: methods that read a graph's structure (are there missing links, isolated clusters, unfilled obligatory slots?) and methods that read a model's internal signals or behavior (does the network's own activity, or its pattern of failures, betray a gap?). This MOC was created 2026-07-07 (first vault-lint flagged the cluster at 6+ notes with no MOC); it also anchors the vault's own open gap-detection question.
Structural signals — reading the knowledge graph
- claim-obligatory-attributes-as-gap-signal — an entity missing a slot its type requires is a machine-readable gap
- claim-kb-completeness-toolkit-cardinality-nca-recall — the formal toolkit beyond obligatory attributes: cardinality assertions, the No-Change Assumption, text-extraction recall
- claim-bridge-detection-lacks-pkg-validation — the honest caveat: the graph algorithms are Tier-1, but "bridge = gap" in a personal knowledge graph has no peer-reviewed validation. The cluster's own skeptic.
- claim-structural-gaps-not-modularity-score-are-gap-signal — the peer-reviewed anchor (Paranyushkin WWW'19, read directly): weakly-connected cluster pairs are the gap signal; the modularity value itself is a bias/diversity index. Sharpens what "modularity as gap signal" may claim.
Behavioral / model-internal signals — reading the system
- claim-query-failure-clustering-as-gap-signal — repeated query failures on a topic are the behavioral signature of a gap
- claim-grade-gradient-rank-gap-detection — GRADE reads gradient-subspace vs hidden-state-subspace rank across layers (needs a backward pass at inference — a deployment cost, links to the economics cluster)
- claim-llm-explicit-implicit-gap-detection — an LLM can distinguish explicit from implicit gaps when scanning a corpus
- claim-llm-neural-metacognition-incomplete — the ceiling: model-internal metacognition is real but partial; don't over-trust it
The vault's own open question
- question-gap-detection — the seed question this cluster answers
- question-consolidation-pass-vs-revisit-protocol — the sibling: once you can find gaps and contradictions, do you fix them targeted (hooks + lint) or globally (a consolidation pass)? The first vault-lint's catch-count is the evidence (see reflection-vault-lint-2026-07-07).
- claim-kgcl-defers-merge-split-topology-changes — the field's current peer-reviewed change vocabulary stops exactly where consolidation would begin: merge/split are explicitly future work.
The through-line
Structural methods find gaps cheaply and mechanically (a Qwen/local-model job); model-internal methods find gaps the structure can't see but cost a forward-or-backward pass. The vault's design bets on the first family for routine hygiene and reserves the second for what links alone miss — the same targeted-first, global-only-if-evidence-demands stance the consolidation question is testing.