How do knowledge graphs work?

Direct answer

Knowledge graphs work by separating entities from the text that describes them, encoding explicit relationships between those entities, and storing the result as a network of typed connections that machines can traverse and query.

This differs from text-based systems where meaning remains implicit in prose, and from databases where relationships are defined by schema design rather than semantic connections.

Entity identification

Knowledge graphs begin by identifying discrete entities within a domain. An entity is anything that exists as a distinct thing: a person, organisation, product, material, concept, or location.

Each entity receives a unique identifier that remains stable even when the entity's description changes. This allows systems to reference the same entity consistently across different contexts and documents.

Entities carry attributes—properties that describe them. For example, a material entity might have attributes for tensile strength, operating temperature, and chemical composition. These attributes differ from relationships because they describe the entity itself rather than how it connects to other entities.

Relationship typing

Relationships define how entities connect to each other. Unlike database foreign keys or document hyperlinks, knowledge graph relationships carry semantic meaning through their type.

A relationship type such as produces, used in, or competes with tells machines what kind of connection exists between two entities. This typing enables machines to distinguish between different kinds of relationships and traverse the graph purposefully rather than arbitrarily.

Relationships are directional. Victrex → produces → PEEK polymer carries different meaning than PEEK polymer → produces → Victrex. The direction matters because it defines which entity acts as subject and which as object in the relationship.

Graph structure

Knowledge graphs store information as networks of nodes (entities) and edges (relationships). This structure differs fundamentally from tables, hierarchies, or documents.

The basic unit is a triple: subject-predicate-object. Victrex → produces → PEEK polymer is a single triple where Victrex is the subject, produces is the predicate (relationship type), and PEEK polymer is the object.

Multiple triples connect to form a network:

Victrex → produces → PEEK polymer
PEEK polymer → used in → medical implants
Medical implants → require → biocompatibility
Biocompatibility → measured by → ISO 10993

This network structure allows machines to follow relationship chains without requiring predefined query paths.

Traversal and querying

Systems query knowledge graphs by traversing relationships between entities. A query such as "which materials does Victrex produce that are used in medical applications?" becomes a traversal problem rather than a text search or table join.

The graph traversal follows three steps:

1. Locate the starting entity. The system identifies the Victrex entity as the query origin point.

2. Follow typed relationships. The system traverses produces relationships from Victrex to identify materials, then follows used in relationships from those materials to applications.

3. Filter results by context. The system returns only materials where the used in relationship points to medical application entities.

The graph structure makes this traversal explicit rather than inferred. Traversal can move in either direction along relationships and can follow multiple relationship types in sequence. This enables complex queries that would require multiple joins in database systems or remain unanswerable in document-based systems.

Updates and evolution

Knowledge graphs evolve by adding or modifying entities and relationships rather than by editing documents or altering database schemas.

When new information emerges, systems add new triples to the graph. When relationships change, systems update or delete specific triples without affecting the broader structure.

This differs from document updates, where changing a single fact requires locating and editing text across multiple files, and from database updates, where schema changes can require restructuring entire tables.

What this enables

Knowledge graphs enable machines to:

• Answer questions that require connecting information across multiple entities
• Identify patterns and relationships that remain invisible in unstructured content
• Maintain consistency across different contexts by referencing stable entity identifiers
• Expand understanding by following relationship chains without human intervention

These capabilities matter when machines need to reason over domain knowledge rather than simply retrieve or search text.