How do knowledge graphs work?

Knowledge graphs are structured databases that represent information as interconnected entities and their relationships. They organize data using nodes (entities) and edges (relationships), forming a network that machines can query and reason about. For example, a knowledge graph might represent “Alice works at Company X” as a node for Alice, a node for Company X, and an edge labeled “works at” connecting them. This structure is often built using standards like RDF (Resource Description Framework) or property graph models (e.g., Neo4j), where each fact is stored as a triple (subject-predicate-object). By explicitly defining relationships, knowledge graphs enable efficient traversal and inference across connected data points.

A key feature of knowledge graphs is their ability to integrate diverse data sources. For instance, a company might combine customer data from a CRM, product details from a catalog, and transaction records into a unified graph. Schema layers, such as ontologies or taxonomies, define rules for how entities relate (e.g., “a Person can be an Employee of an Organization”). This allows the graph to enforce consistency and support automated reasoning. For example, if the graph knows “Company X is headquartered in New York” and “New York is in the USA,” it can infer “Company X is located in the USA” without explicit storage. Entity resolution—linking “Alice Smith” in one dataset to “A. Smith” in another—is also critical for avoiding duplicates and maintaining accuracy.

Developers interact with knowledge graphs using query languages like SPARQL (for RDF-based graphs) or Cypher (for property graphs). For example, a Cypher query might find all employees at Company X who joined after 2020: MATCH (p:Person)-[r:WORKS_AT]->(c:Company {name: "Company X"}) WHERE r.start_year > 2020 RETURN p. Knowledge graphs power applications like search engines (Google’s Knowledge Graph provides instant answers) or recommendation systems (linking products to user preferences). They excel at scenarios requiring contextual understanding, such as fraud detection (identifying suspicious transaction patterns) or supply chain optimization (mapping supplier dependencies). For developers, their flexibility in modeling complex relationships and combining structured and unstructured data makes them a practical tool for solving interconnected data challenges.