What is a Knowledge Graph?

A knowledge graph is a structured representation of information as a network of entities (nodes) connected by typed relationships (edges). Instead of storing facts as rows in tables or unstructured text in documents, a knowledge graph stores them as statements of the form (subject, relationship, object) — for example, (Acme Corp, supplies, Northwind Ltd), (Invoice 4421, references, Purchase Order 8870), (Contract C-112, expires_on, 2026-12-31).

That shape makes knowledge graphs the backbone of enterprise reasoning systems. A large language model can generate fluent prose about a company's supply chain, but only a knowledge graph can reliably answer "which contracts expire in the next 90 days and depend on a supplier with a compliance flag?" in a single traversal.

How a Knowledge Graph Works

Three components define the structure:

1. Entities (nodes) represent things of interest — customers, contracts, products, employees, documents, events. Each entity has a unique identifier and a set of attributes.
2. Relationships (edges) describe how entities connect — employs, supplies, references, is_a, depends_on. Relationships are typed and directional, and often carry attributes of their own (start date, confidence score, source).
3. Ontology (schema) defines the allowed entity types and relationship types. A good ontology constrains what can be said — preventing nonsense statements like (Invoice, manages, Employee) — while leaving room for the messy reality of enterprise data.

Data enters the graph from three sources: structured systems (ERPs, CRMs, databases), semi-structured sources (API responses, logs), and unstructured sources (contracts, emails, tickets) via information extraction with LLMs or document AI.

Knowledge Graph vs Vector Database

Retrieval-augmented generation exploded the popularity of vector databases. Many teams now ask whether they need a knowledge graph, a vector database, or both.

Most production systems use both. A vector database retrieves the right passage from 10,000 contracts; a knowledge graph enforces that the retrieved passage belongs to a contract with the right counterparty, status, and jurisdiction.

Enterprise Use Cases

Supply chain intelligence. A manufacturer maps suppliers, parts, facilities, and regulations into a graph. When a tariff or natural disaster hits, the graph traverses suppliers-of-suppliers three hops deep and surfaces parts at risk within minutes — a question that used to take a procurement analyst two days.

Contract and compliance reasoning. Finance and legal teams load contracts, counterparties, entitlements, and obligations into a graph. Agents answer questions like "which renewals in Q2 have auto-renewal clauses and penalty ceilings under $50,000" by traversing the graph. This is the contract intelligence pattern used by large enterprise deployments.

Customer 360 for sales and support. A single customer node connects to accounts, contacts, tickets, contracts, product usage, and renewals. When a support AI agent takes a ticket, it pulls the customer's full context in one query rather than stitching together five API calls.

Fraud and anomaly detection. Transactions, accounts, devices, and counterparties form a graph where fraud rings show up as dense subgraphs. Banks detect mule networks and laundering structures that rule-based systems miss.

Product knowledge assembly. Engineering, manufacturing, and service data about a product live in a graph, letting AI assistants answer "which bill of materials changes since last quarter affect this warranty claim?"

Enterprise Implementations

Public examples of production knowledge graphs at scale:

• Google Knowledge Graph. Powers the info panels in Google Search, with hundreds of billions of facts across people, places, things.
• Amazon Product Graph. Connects products, categories, attributes, and customer signals to drive recommendations and search.
• LinkedIn Economic Graph. Maps members, companies, skills, schools, and jobs — the foundation for every LinkedIn recommendation surface.
• Uber Michelangelo / Linq. Uber publicly described its knowledge graph for connecting trips, riders, drivers, cities, and operations data.

The pattern is consistent: graphs are the substrate that makes machine learning interpretable and auditable at enterprise scale.

When NOT to Use a Knowledge Graph

• Your problem is semantic search over text. If your use case is "find passages similar to this query," a vector database alone is simpler and cheaper. Add a graph only when you need structured reasoning.
• Your data is already well-modeled in a relational database. A clean Postgres schema with foreign keys is already a graph in disguise. Don't add a second system unless you need deep multi-hop traversals (3+ joins per query) that kill SQL performance.
• You cannot invest in ontology work. Knowledge graphs fail silently when the schema is wrong. Without a team willing to define entity types, relationship types, and data quality rules, the graph degrades into noise.
• Your update rate outpaces your extraction pipeline. If entities change faster than your ingestion job can keep up, the graph will mislead. Build the streaming pipeline first.

Getting Started

1. Pick one reasoning question that currently takes hours of analyst time — supply chain, contracts, or customer context are common starting points.
2. Model the minimum ontology needed to answer that question. Three to five entity types and five to ten relationship types is enough for a first version.
3. Load structured data first. Start with your ERP, CRM, or product catalog. Add unstructured sources only after the structured layer works.
4. Choose infrastructure based on scale. Neo4j, Amazon Neptune, and TigerGraph are the common graph databases. For smaller graphs, PostgreSQL with the ltree or pg_graph extension works. For RAG integration, GraphRAG (Microsoft) and LlamaIndex provide off-the-shelf patterns.
5. Wire it to an agent or RAG layer so humans experience the graph through natural-language questions, not Cypher queries.

FAQ

Is a knowledge graph the same as a database?

A knowledge graph can be stored in a graph database, a relational database, or a document store — the distinction is in the model, not the storage. The defining property is that it represents information as entities and typed relationships, optimized for traversal and reasoning rather than tabular aggregation.

How does a knowledge graph help with RAG?

Vanilla RAG retrieves text chunks by semantic similarity, which can return irrelevant or conflicting passages. Graph-enhanced RAG uses the knowledge graph to filter and rank retrievals — for example, returning only passages from contracts with the correct counterparty or product line. This improves precision dramatically on enterprise queries that require multi-hop reasoning.

Do I need an LLM to build a knowledge graph?

No. Knowledge graphs predate LLMs by decades and can be built entirely from structured data. LLMs help in two places: extracting entities and relationships from unstructured text at scale, and letting users query the graph in natural language instead of Cypher or SPARQL. Both are accelerators, not requirements.