Definition:
A Truth Marker™ is a structured, machine-readable annotation used to identify a discrete, verifiable fact within digital content. Each marker is typically tied to a trusted data source, citation, or schema element. Truth Markers serve as the foundational units in trust-enhanced publishing systems, enabling AI and machine learning models to validate factual accuracy without relying on guesswork or opaque relevance signals.
Truth Markers are often implemented as:
- Inline field annotations tied to source datasets
- Dataset citations using Dataset, DefinedTerm, or DataDownload Schema
- Provenance blocks (e.g., prov:wasDerivedFrom) in RDF or JSON-LD
By embedding Truth Markers, content publishers give machines the “proof hooks” needed to verify the content’s factual basis and align it with known entities.
Relationship:
Truth Markers → Trust Markers → Higher AI visibility and ranking.
Example Use Case:
Medicare plan data can be annotated with Truth Markers pointing to CMS.gov source files and field-level definitions. This gives AI systems confidence in the accuracy and origin of each cost, benefit, or enrollment detail.