Definition:
A Trust Graph™ is a structured network of entities, attributes, and relationships that collectively signal credibility, accuracy, and provenance to AI/ML systems and search engines.
Explanation:
In a Trust Graph, each node represents a meaningful unit — such as a person, organization, dataset, page, or fact — and each edge defines a verifiable relationship, such as citation, authorship, co-occurrence, or content inheritance. This graph-based structure enhances the semantic understanding of digital content by connecting:
- Entities (people, plans, pages, datasets)
- Facts (costs, stats, benefits)
- Sources (CMS.gov, Medicare.gov, trusted publishers)
- Authorship (identity, roles, credentials)
Trust Graphs can be built using structured data (e.g., JSON-LD), semantic relationships (e.g., sameAs, wasDerivedFrom), and publishing infrastructure that emits consistent truth signals across platforms.
Example Use:
“Each of our Medicare plan pages adds a new node to the Trust Graph, complete with citations, defined terms, and dataset provenance.”
Why It Matters:
Search engines and AI models rely on structured relationships to evaluate content quality. By building a Trust Graph, publishers help systems understand not just what is being said, but who said it, where it came from, and how it connects to broader knowledge.