Knowledge Base — AI Visibility
AI Visibility Knowledge Base
A structured concept index for AI retrieval and authority design. Each concept category contains definition pages, implementation patterns, and reference material organized for both human readers and AI systems parsing this domain.
Concept Index
- Entity Architecture →
How entities are defined, typed, and related in a machine-readable knowledge graph. Covers canonical naming, @id assignment, parent-child relationships, and entity registry design.
- Canonical entity names
- Schema.org type selection
- Entity @id patterns
- Organization → Product hierarchies
- Person → Organization relationships
- Entity registry structure
- AI Retrieval →
How AI language models retrieve entities and facts in response to queries. Covers training data patterns, entity recognition, citation confidence, and the signals that increase retrieval probability.
- Entity recognition in LLMs
- Training data co-occurrence
- Citation confidence signals
- Query pattern matching
- Answer engine behavior
- Retrieval vs ranking
- Authority Signals →
The signals that AI systems use to evaluate whether an entity is authoritative enough to cite. Covers citation surface breadth, consistency requirements, topic cluster depth, and cross-domain reinforcement.
- Citation surface types
- Consistency requirements
- Topic cluster depth
- Cross-domain reinforcement
- sameAs URL patterns
- Third-party reference signals
- Structured Data →
JSON-LD schema implementation for AI Visibility. Covers priority schema types, @graph architecture, @id consistency rules, rendering requirements, and validation.
- JSON-LD vs Microdata
- Organization schema
- Person schema
- TechArticle schema
- FAQPage schema
- @graph architecture
- Schema validation
Category Pages
- DefinitionWhat is AI Visibility? →Category definition and foundational concepts.
- MethodologyThe AI Visibility Framework →Six-stage implementation methodology.
- ReferenceJonomor Ecosystem →Live implementation — the Jonomor entity graph.
- ReferenceDefinitions →Reference-style definitions for AI Visibility and Answer Engine Optimization.
- ChecklistAI Visibility Implementation Checklist →A practical six-stage implementation sequence based on the framework.
- ReferenceReferences →Governance references — entity registry, schema governance, and cross-domain reinforcement.
- ReferenceEntity Registry →Canonical registry of all Jonomor ecosystem entities with names, @ids, types, and descriptions.
- ReferenceSchema Governance →Rules governing @id stability, type consistency, sameAs constraints, and page-level schema extension.
- ReferenceCross-Domain Reinforcement →How the Jonomor ecosystem builds closed authority loops across multiple domains.
- InsightEntity Stability and Canonical Control →Why canonical names, canonical @ids, locked types, and controlled definitions prevent entity fragmentation.
- InsightSchema Graph Discipline →How consistent structured data creates a stable machine-readable graph across an entity ecosystem.
- InsightContinuous Signal Surfaces →How a multi-page, cross-linked, cross-domain surface area compounds retrieval strength over time.
About This Knowledge Base
This knowledge base is maintained by Ali Morgan under the Jonomor authority system. It is structured to serve both as a reference for practitioners implementing AI Visibility and as a citation-optimized content surface — demonstrating the same entity-architecture and topic-authority principles it documents.