Neo4j

A property graph stores entities as nodes and typed relationships between them, with properties on both. Neo4j's Cypher language expresses patterns such as finding components connected to a supplier or traversing a dependency chain. Indexes and constraints support efficient lookup and data integrity. This is different from an embedding store, whose main operation compares vector representations, although graph and vector capabilities can coexist. A knowledge graph built in Neo4j additionally requires meaningful entity identities, relation semantics and provenance; the database alone does not supply those modeling decisions.

The practitioner defines labels, relationship types, identifiers and constraints before ingesting data. Query tests verify both results and traversal boundaries, especially where cycles or high-degree nodes can expand work. Application access is restricted to allowed operations, and natural-language-generated Cypher is validated before execution. Useful artifacts include the graph schema, ingestion mapping, tested queries and source provenance. Performance evaluation uses representative graph shapes rather than only small examples, since a query that looks simple can touch many relationships in a real collection.

Incorrect entity merging or poorly defined relationships can produce confidently wrong traversals. Graph queries also require attention to cardinality, privileges and query cost. Neo4j is a particular database implementation, while knowledge graphs and GraphRAG are broader modeling and application approaches. Choosing it should follow a demonstrated need for connected-data queries and operational requirements. A graph structure does not make extracted facts true or remove the need to maintain their provenance.