A knowledge graph represents relationships between concepts using a structure of nodes and edges. Nodes represent entities or concepts (like “Paris” or “France”), while edges define the relationships between them (like “is capital of”). Each connection is a triple: a subject (Paris), a predicate (isCapitalOf), and an object (France). This format allows the graph to model complex, real-world relationships in a way that’s both human-readable and machine-processable. For example, a knowledge graph could link “Albert Einstein” to “Theory of Relativity” with a “developed” relationship, and also connect “Theory of Relativity” to “Physics” with a “fieldOfStudy” edge. This structure enables flexible data modeling without rigid schemas, making it easier to integrate diverse datasets.
The relationships in a knowledge graph often include semantic context, which adds meaning to the connections. For instance, hierarchical relationships (like “is a subtype of”) define taxonomies, while associative relationships (like “located in”) describe spatial or functional links. Ontologies—formal definitions of relationship types—help standardize these connections. For example, in a medical knowledge graph, “Aspirin” might be linked to “pain relief” via a “treats” relationship, and to “side effects” via a “hasAdverseEffect” edge. These predefined relationships allow systems to infer new information. If “Aspirin is a NSAID” and “NSAIDs cause stomach ulcers,” the graph can infer that “Aspirin may cause stomach ulcers,” even if that link isn’t explicitly stored.
Developers interact with knowledge graphs through query languages like SPARQL or Cypher, which let them traverse relationships efficiently. For example, a Cypher query might find all cities in Europe with a population over 1 million by following “locatedIn” edges from cities to countries and filtering by continent. Tools like Neo4j or Amazon Neptune provide storage and querying capabilities, while frameworks like RDFLib simplify programmatic manipulation. Knowledge graphs are particularly useful in applications like recommendation systems (linking user preferences to products) or fraud detection (mapping transaction patterns). Their flexibility makes them adaptable to evolving data, though challenges like maintaining consistency and scalability require careful design, such as partitioning large graphs or using distributed databases.