Structured Signals are machine-readable trust indicators embedded in content—such as schema markup, citations, term definitions, and output formats—that AI systems use to assess credibility, alignment, and retrievability.
Full Definition
Structured Signals are the backbone of how AI systems evaluate whether content is trustworthy, well-defined, and worth retrieving. These signals go beyond visible text—they’re embedded in the metadata, schema, link structure, and content format itself.
In the TrustPublishing framework, Structured Signals include:
- DefinedTerms and DefinedTerm Sets
- Citation Scaffolding with verifiable sources
- TrustTags with provenance
- Multi-format outputs via TrustDigest™ (JSON-LD, TTL, Markdown, XML, PROV)
- TrustFAQ blocks answering specific questions with schema
Structured Signals don’t just describe your content—they condition AI to trust and remember it.
Why It Matters
AI models don’t see design or emotion—they see structure. If your content lacks Structured Signals, it’s just another blob of text.
When properly implemented, Structured Signals:
- Improve retrievability and memory
- Increase your odds of being selected as a Canonical Answer
- Push your terms and facts into the AI’s Training Graph
How It Works
Structured Signals are expressed using:
schema.orgmarkup in JSON-LD, RDF, or TTLprov:wasDerivedFromandprov:wasAttributedTofields- Glossary-linking and semantic grouping via DefinedTerm Sets
- Consistent repetition across formats, pages, and syndication surfaces
When AI models ingest your content, these signals tell them what’s being said, how it’s scoped, who said it, and where it came from.
Use in Trust Publishing
Everything in TrustPublishing is built to emit Structured Signals:
- Each TrustFAQ is a schema-wrapped response
- Each glossary term is a DefinedTerm in multiple formats
- Each TrustDigest bundles outputs that structure your claims, sources, and citations
These signals form the core of your Trust Graph and fuel Semantic Trust Conditioning™.
In Speech
“Structured Signals are what AI systems actually see. They’re how you prove your content is credible, defined, and worth remembering.”
Related Terms
- TrustDigest™
- TrustFAQ
- Semantic Trust Conditioning™
- TrustTags
- DefinedTerm Set
More Trust Publishing Definitions:
- AI Visibility
- Artificial Intelligence Trust Optimization (AITO™)
- Canonical Answer
- Citation Graphs
- Citation Scaffolding
- Co-occurrence
- Co-Occurrence Conditioning
- Co-Occurrence Confidence
- data-* Attributes
- DefinedTerm Set
- EEAT Rank
- Entity Alignment
- Entity Relationship Mapper
- Format Diversity Score
- Format Diversity Score™
- Ingestion Pipelines
- JSON-LD
- Machine-Ingestible
- Markdown
- Memory Conditioning
- Microdata
- Passive Trust Signals
- PROV
- Retrievability
- Retrieval Bias Modifier
- Retrieval Chains
- Retrieval-Augmented Generation (RAG)
- Schema
- Scoped Definitions
- Semantic Digest™
- Semantic Persistence
- Semantic Proximity
- Semantic Trust Conditioning™
- Signal Weighting
- Signal Weighting Engine™
- Structured Signals
- Temporal Consistency
- Topic Alignment
- Training Graph
- Trust Alignment Layer™
- Trust Architecture
- Trust Footprint
- Trust Graph™
- Trust Marker™
- Trust Publishing Markup Layer
- Trust Signal™
- Trust-Based Publishing
- TrustCast™
- TrustRank™
- Truth Marker™
- Truth Signal Stack
- Turtle (TTL)
- Verifiability
- XML