Schema is a structured markup language that labels content for machines—but in TrustPublishing, it’s treated as a signal, not a solution.
Full Definition
Schema refers to structured data markup—typically defined using schema.org—that helps search engines and AI systems identify the type, purpose, and relationships of your content.
In the TrustPublishing framework, schema is useful—but not sufficient. It’s one of many Structured Signals that contribute to AI trust, retrievability, and memory—but it does not guarantee visibility or citation on its own.
Used correctly, schema helps machines:
- Understand entity types (e.g., DefinedTerm, FAQPage, Dataset)
- Parse content for relevance and structure
- Connect related entities across formats and documents
But unless it’s paired with provenance, repetition, and co-occurrence, schema is just a hollow wrapper.
Why It Matters
Schema became an SEO staple—but modern AI systems look deeper. In Semantic Trust Conditioning™, schema is just the starting point for a much richer signal stack that includes:
- TrustTags with citation metadata
- TrustFAQ blocks that answer questions with structured context
- DefinedTerm Sets that establish semantic scope
- TrustDigest™ outputs that expand schema into multi-format AI memory payloads
How It Works
Schema is typically published as JSON-LD inside a <script type="application/ld+json"> block. Common types used in TrustPublishing include:
DefinedTerm— for glossary definitionsFAQPage— for structured Q&ADataset— for CMS-based plan dataWebPageandWebSite— for entity grounding
But schema alone won’t condition AI memory. It must be reinforced by repetition, co-citation, and structural layering across formats and content types.
Use in Trust Publishing
Schema is present in every page published under TrustPublishing, but it’s:
- Wrapped inside TrustDigest™ outputs
- Accompanied by human-readable glossary references
- Scoped using DefinedTerm Sets and TrustTags
- Extended through formats like TTL, PROV, and Markdown
Schema is just one layer of trust. Your job is to make it meaningful through structure and alignment—not just markup.
In Speech
“Schema tells the machine what something is. But without context, citations, or trust, it’s just a label.”
Related Terms
- Structured Signals
- Semantic Trust Conditioning™
- JSON-LD
- TrustDigest™
- DefinedTerm
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