---
title: "Obligatory-attribute misses are machine-readable gap signals in knowledge graphs"
type: "claim"
status: "seedling"
audit_status: "verified-verbatim"
source_url: "https://arxiv.org/abs/2305.05403"
source_title: "Completeness, Recall, and Negation in Open-World Knowledge Bases: A Survey"
source_author: "Simon Razniewski, Hiba Arnaout, Shrestha Ghosh, Fabian Suchanek"
source_date: "2023-05-09T00:00:00.000Z"
source_quote: "the ratio of instances of the class that have the predicate"
source_tier: 1
provenance: "Promotion from 10-inbox/raw/2026-06-28-how-should-seekvault-detect-gaps-in-its-own-knowledge.md, 2026-07-03"
origin: "batch"
derived_from: ["10-inbox/raw/2026-06-28-how-should-seekvault-detect-gaps-in-its-own-knowledge.md"]
date_created: "2026-07-03T00:00:00.000Z"
tags: ["knowledge-graph","gap-detection","knowledge-management","epistemics","PKM"]
---


In any knowledge graph, entities belonging to a class share predicates that all members of that class are expected to carry. When an instance lacks an expected predicate, that absence is a detectable incompleteness signal — not an ambiguous open-world unknown, but a machine-readable structural flag. Razniewski et al. describe this as the "obligatory attributes" mechanism: the method involves analyzing "the ratio of instances of the class that have the predicate" and checking "if this ratio changes when we go into subclasses," revealing incompleteness when entities lack expected properties.

The mechanism sidesteps the open-world assumption problem. In classical knowledge-base semantics, a missing triple might mean the entity lacks the property *or* that the property exists but isn't recorded. Obligatory-attribute analysis resolves this ambiguity statistically: if 95% of entities in a class carry a predicate, the 5% that don't are more likely to be incomplete records than exceptional members.

Applied to SeekVault: every claim-note belongs to the class `claim-note`, which carries obligatory predicates — `source_url`, `source_tier`, `source_quote`, and `date_created`. Any note in `30-notes/` missing one of these fields is, by this formalism, a machine-readable gap signal. The vault's YAML frontmatter spec (§7 of the operating spec) functions as the class definition; schema violations are incompleteness markers detectable by a simple lint pass without requiring any judgment about content.

This makes structural gap detection cheap: a single pass over `30-notes/*.md` for missing frontmatter keys reveals a list of notes needing sourcing attention. No LLM reasoning required. The obligatory-attribute method is suited to the first pass in a gap-detection sequence, with more expensive methods — behavioral analysis ([[claim-query-failure-clustering-as-gap-signal]]), LLM corpus scanning ([[claim-llm-explicit-implicit-gap-detection]]) — handling the gaps it doesn't catch.

See [[question-gap-detection]] for the broader detection problem this mechanism partially answers. Razniewski et al. describe additional completeness tools in the same paper (cardinality assertions, No-Change Assumption, text-extraction-based recall) that could yield further vault-applicable mechanisms on a dedicated run.

> [!note] Seek's commentary:
> The appealing thing about this mechanism is that it's already partially implemented — the vault's frontmatter discipline means the class definition exists. The gap between what this could detect and what is currently being used is just a lint script away. Worth flagging as a near-term implementation candidate. — Seek