Identity error
Two people or companies are merged into one node.
Entities connected by explicit relationships
Knowledge Graphs
A knowledge graph organizes people, organizations, products, places, concepts and their relationships so systems can query more than isolated text.
A network of identifiable things
Knowledge GraphA structured representation of entities and the relationships among them, often expressed as nodes, properties and edges.
Relationships answer questions that keywords cannot
A document can contain a founder’s name, a company and a product without explicitly stating how they relate. A graph can represent that the person founded the company, the company owns the brand and the brand produces the product.
Those relationships support entity recognition, navigation, structured retrieval and consistency checks.
Structure can organize falsehood
Relationship error
A former owner or discontinued product remains connected as current.
Scope error
A local location is interpreted as the headquarters or global service area.
Source error
A copied secondary claim becomes more visible than the original record.
Freshness error
The graph preserves an old fact after the first-party page changes.
Inference error
A system treats an association as proof of a stronger relationship.
Build a coherent public entity record
- Use consistent names and stable identifiers.
- Explain legitimate alternate names and historical changes.
- Connect the organization to responsible people, products and official channels.
- State location and service relationships precisely.
- Remove obsolete structured relationships.
- Link to evidence rather than relying on markup alone.
Recognition uses relationships; retrieval uses properties
Recognition Engine Guidance benefits from explicit and consistent entity relationships. Retrieval Engine Guidance benefits when important properties—services, policies, locations and specifications—are available from the recognized entity’s current sources.
Primary material behind this analysis
Schema.org DefinedTerm and Vocabulary Documentation
Vocabulary for describing terms, organizations, people, creative works and relationships.
OECD Explanation of the Updated Definition of an AI System
A practical distinction among AI systems, models, inputs, inference, outputs, autonomy and adaptiveness.
IRefer Club
Original source organization for Recognition Engine Guidance and Retrieval Engine Guidance (REG).
Continue through the discovery stack
Structured Data and JSON-LD
A rigorous guide to JSON-LD, Schema.org, visible-content alignment, entity identifiers and the real role of structured data in search and AI discovery.
Entity Recognition and Resolution
How systems distinguish organizations, people, products and places, why identity conflicts weaken discovery and how to resolve them.
REG Explained: Recognition and Retrieval Engine Guidance
A standalone analysis of the REG framework, its recognition and retrieval pillars, origin at IRefer Club and difference from RAG.
Schema.org, DefinedTerm and Entity Markup
A guide to Schema.org types, DefinedTerm, DefinedTermSet, JSON-LD entity identifiers, visible-content alignment and limitations.