← AI Terminology
Knowledge Graph
A knowledge graph represents facts as a network of entities (nodes) and relationships (edges), often with types and properties, enabling structured queries and multi-hop traversal.
Used in search, RAG, enterprise data fabrics, and classic semantic web stacks.
Used in search, RAG, enterprise data fabrics, and classic semantic web stacks.
Why It Matters in AI
Unstructured text hides relationships; graphs make them queryable and auditable. Combined with LLMs for extraction and NL2query, graphs ground answers in explicit structure — key for compliance-heavy domains.
Key Points
| Aspect | Description |
|---|---|
| Model | Entities, relations, attributes; RDF/property graphs |
| Query | Cypher, SPARQL, Gremlin |
| Stores | Neo4j, Amazon Neptune, RDF triple stores |
| AI role | LLM entity/relation extraction; graph-enhanced RAG |
| Benefit | Explainable paths; deduplicated entities |
| Related | GraphRAG, ontologies, link prediction |
Simple Analogy
A detective’s corkboard: people and companies as pins, strings for relationships you can follow with your finger.
Common Usage Examples
- Neo4j Cypher:
MATCH (a)-[r]->(b) - LLM extract triples into a graph
- GraphRAG community detection
- Enterprise “customer 360” graphs
Summary
In short: A knowledge graph stores facts as linked entities and relations — structured memory that LLMs can query and explain.