@prefix this: . @prefix sub: . @prefix schema: . @prefix np: . @prefix dct: . @prefix pav: . @prefix nt: . @prefix npx: . @prefix xsd: . @prefix rdfs: . @prefix prov: . @prefix wd: . @prefix foaf: . sub:Head { this: a np:Nanopublication; np:hasAssertion sub:assertion; np:hasProvenance sub:provenance; np:hasPublicationInfo sub:pubinfo . } sub:assertion { sub:ai-and-the-evolving-subjective a wd:Q604733; dct:description "

Early Lessons from a Work in Progress

The Challenge

  • Knowledge graphs represent what is true, but in domains like music and content classification, truth is contested, evolving, and subjective.
  • When systems flatten ambiguity, they lose organizational intelligence.
  • With AI agents now consuming the graphs, the problem is urgent.
  • Agents can't distinguish institutionally-ratified claims from contested ones, producing not hallucination but false consensus.
  • Emerging standards like OKF standardize a grain for structural metadata, but provide no vocabulary for epistemic metadata or typed relation: confidence, verification status, decision provenance, temporal validity.

A Solution

We propose a working augmentation: an approach that borrows both from OKF and RDF, but with separable conventions for the dimensions that emerge through collaboration at scale.

What we did

  • What we built.
  • The ambiguous situations that shaped each decision.

What we learned — including why inconsistency between sources is often signal, not noise.

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