Knowledge Graph

A knowledge graph is a structured way of storing information that captures not just individual facts, but the relationships between them. Instead of storing data in flat tables or documents, a knowledge graph represents information as a network of connected entities — making it possible to navigate from one concept to related concepts, much like your brain connects ideas.

How it works: nodes and edges

A knowledge graph is built from two simple building blocks:

• Nodes (also called entities): The things in your graph. A node might be a person, a company, a product, a concept, or a location. Each node has properties — a person node might have a name, a job title, and a department.
• Edges (also called relationships): The connections between nodes. Edges describe how two nodes are related. "Sarah → works at → Acme Corp." "Acme Corp → is located in → London." "Sarah → manages → Project Alpha."

That is it. Nodes and edges. But from these simple building blocks, you can represent remarkably complex information structures. And crucially, you can traverse the graph — starting from any node and following edges to discover related information.

Famous examples

Knowledge graphs are more common than you might think:

• Google Knowledge Graph: When you search "Albert Einstein" and see a panel showing his birth date, nationality, famous works, and related scientists — that information comes from Google's knowledge graph, which contains billions of connected facts.
• Wikidata: An open, community-maintained knowledge graph that structures Wikipedia's information as connected entities. It contains over 100 million items with relationships between them.
• Enterprise knowledge graphs: Many large organisations build internal knowledge graphs that connect employees, projects, skills, documents, and business processes.

How AI uses knowledge graphs

Knowledge graphs enhance AI in several important ways:

• Grounding AI responses: When an AI answers a question, a knowledge graph can provide verified, structured facts to ground the response — reducing hallucination.
• Enhancing RAG systems: Instead of just retrieving text passages, a RAG system backed by a knowledge graph can follow relationships to gather contextually relevant information that might not share keywords with the query.
• Reasoning about relationships: "Who are the managers in the London office who have worked on AI projects?" requires understanding three relationships (manager-of, located-in, worked-on). A knowledge graph makes this query natural; a traditional database makes it complex.
• Entity disambiguation: When an AI encounters "Apple," a knowledge graph helps determine whether the context refers to the company, the fruit, or the record label — by examining the relationships around the entity.

Knowledge graphs and RAG

One of the most promising applications is combining knowledge graphs with RAG (Retrieval-Augmented Generation). In a standard RAG system, the AI retrieves text passages that are semantically similar to the query. In a graph-enhanced RAG system, the AI can also follow relationships in the knowledge graph to find related information that is contextually relevant but might not share similar wording.

For example, if someone asks "What are the risks of Project Alpha?", graph-enhanced RAG could retrieve not just documents mentioning Project Alpha, but also information about related projects that had similar characteristics, the team members involved (and their areas of expertise), and relevant company policies — all by following edges in the graph.

When to use a knowledge graph

Knowledge graphs add the most value when:

• Your data has rich, important relationships between entities
• Users need to ask questions that require connecting multiple pieces of information
• You need to maintain a single source of truth for entity information across multiple systems
• You want to enhance AI retrieval with relationship-aware search

They are less necessary when your data is straightforward, relationships are simple, or basic text search meets your needs.

Getting started

Building a knowledge graph does not require exotic technology. Graph databases like Neo4j, Amazon Neptune, and ArangoDB provide the storage layer. The harder part is defining your schema (what types of nodes and relationships exist) and populating the graph with accurate data. Many organisations start small — graphing one domain (products, customers, or documentation) — and expand as value is demonstrated.