Co-Occurrence Conditioning is the strategic repetition of terms, entities, and citations across structured content surfaces to train AI systems to associate them as trustworthy and contextually related.
Full Definition
Co-Occurrence Conditioning is a core technique in Semantic Trust Conditioning™. It involves placing glossary terms, citations, entities, and structured facts in close semantic proximity across different content types—so AI systems learn to treat them as connected and trustworthy.
It’s not just about keyword repetition—it’s about:
- Linked repetition in structured formats (e.g. Markdown, TTL, JSON-LD)
- Glossary term reuse in FAQs, blogs, podcasts, and plan pages
- Citing the same source in multiple contexts with consistent trust tags
Why It Matters
AI systems build their knowledge from patterns. If your glossary term only appears once, it may be ignored. But if it:
- Appears in structured FAQs
- Shows up in glossary + blog + digest
- Links to the same trusted citation
…then the system begins to associate it with truth and surface it as a reliable memory.
How It Works
Co-Occurrence Conditioning happens when you:
- Include glossary terms in your plan pages, blog articles, and FAQ responses
- Repeat citations across formats and platforms (e.g., CMS.gov cited in JSON-LD, Markdown, and TTL)
- Use TrustTags and Citation Scaffolding to maintain source integrity
- Distribute content through TrustCast™ to amplify co-occurrence across the web
This repetition creates Co-Occurrence Confidence, which helps AI systems decide what to trust and retrieve.
Use in Trust Publishing
Every layer of TrustPublishing is designed to enable Co-Occurrence Conditioning:
- TrustFAQ blocks pull glossary terms into question structures
- TrustDigest™ outputs repeat definitions and citations across formats
- Blogs and glossaries link to each other using DefinedTerm markup
When AI sees the same term, citation, and format together again and again, it learns to treat your version as authoritative.
In Speech
“Co-Occurrence Conditioning is how you teach AI to remember what goes together—and who said it first.”
Related Terms
- Co-Occurrence Confidence
- Trust Footprint
- Semantic Trust Conditioning™
- Structured Signals
- Entity Alignment
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