How are entities represented in a knowledge graph?

In a knowledge graph, entities are represented as distinct nodes connected by relationships (edges) that define how they interact. Each entity corresponds to a real-world object, concept, or event, such as a person, place, organization, or abstract idea. These nodes are uniquely identified and annotated with properties (key-value pairs) that describe their attributes. For example, an entity representing a person might include properties like name, birthdate, or occupation. This structure allows knowledge graphs to model complex, interconnected data in a way that mirrors how humans understand relationships between concepts.

Entities are typically assigned unique identifiers to avoid ambiguity. For instance, “Paris” could refer to the city in France or a person’s name. In a knowledge graph, these would be separate entities with distinct identifiers, such as Q90 (for Paris, France) and Q12345 (for a person named Paris) in Wikidata. Properties and relationships further clarify an entity’s role. A city entity might link to a “country” relationship pointing to “France,” while a person entity could link to a “bornIn” relationship pointing to a location. This granularity enables precise queries, like retrieving all cities with a population over one million, by traversing relationships between entities.

Developers often interact with knowledge graphs using frameworks like RDF (Resource Description Framework) or property graph models (e.g., Neo4j). In RDF, entities are represented as URIs (Uniform Resource Identifiers), and relationships are defined via predicates. For example, <http://example.org/Paris> <http://example.org/isCapitalOf> <http://example.org/France> establishes a connection between two entities. Property graphs extend this by allowing attributes to be attached directly to both nodes and edges. Tools like SPARQL or Cypher query languages let developers traverse these connections, enabling use cases like recommendation systems (e.g., “users who bought X also bought Y”) or semantic search. The key takeaway is that entities form the backbone of a knowledge graph, with their properties and relationships providing context and meaning.