Knowledge Graphs for AI: Practical Guide

A knowledge graph is a structured representation of entities and relationships in a domain. For AI systems, it's the foundation that enables reliable retrieval, consistent reasoning, and composable agent behavior.

What a knowledge graph is (and isn't)

A knowledge graph is not a database schema. It's not a document index. It's not a vector store. It's a semantic graph — nodes representing entities, edges representing typed relationships between them, and properties attached to both.

The distinction matters because each structure enables different retrieval patterns. A database gives you precise lookups. A vector store gives you semantic similarity. A knowledge graph gives you traversal — "find everything related to X via this relationship type, filtered by these properties." For agent reasoning, traversal is often exactly what's needed.

The minimal structure

Entities — things that exist. Every entity has a type, an identifier, a set of properties, and a set of relationships to other entities. The type constrains which properties and relationships are valid.

Relationships — named, directed connections between entities. "Customer subscribes to Product." "Feature depends on Integration." "Incident caused by Deployment." The direction and name of the relationship carries semantic meaning that undirected adjacency doesn't.

Claims — assertions about entities with provenance. Claims separate what's known from what's asserted. "Product X supports SSO" is a claim. Claims can be sourced, timestamped, and conditioned — "as of version 2.4, with Enterprise tier."

Sources — the origin of claims. Every claim should be traceable. Sources can be documents, APIs, expert assertions, or derived inferences. Traceability enables agents to explain their reasoning and enables humans to audit outputs.

Agent consumption patterns

Agents consume knowledge graphs through three primary patterns: lookup (retrieve an entity by identifier), traversal (follow a relationship path from a starting entity), and query (find entities matching a property filter).

Designing tools for each pattern means agents can reason precisely: "what do we know about this entity" (lookup), "what does this entity connect to" (traversal), "which entities have this property" (query). Each pattern maps to a specific tool schema.

Building one in practice

Start with entity types, not instances. Define the types that exist in your domain before populating them. This forces the schema question before the data question.

Define relationships explicitly. Every relationship type should have a name, a source entity type, a target entity type, and an optional set of properties on the edge itself. Undirected relationships ("A relates to B") are almost always wrong — the direction carries meaning.

Populate claims with provenance from the start. Retrofitting provenance to a knowledge graph is expensive. Build the tracing structure into the initial schema.