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.