What are the main components of a knowledge graph?

A knowledge graph is a structured way to represent real-world information using three core components: entities, relationships, and schemas/ontologies. Entities are the distinct objects, concepts, or things in the graph, such as people, places, or organizations. Each entity is uniquely identified (e.g., “Paris” as a city vs. “Paris” as a person) and has attributes (e.g., population, founding date). Relationships define how entities connect—for example, “locatedIn” could link “Eiffel Tower” to “Paris.” Schemas or ontologies act as blueprints, defining the types of entities (like “Person” or “Company”) and valid relationships between them (e.g., “employeeOf” connects a “Person” to a “Company”). These schemas ensure consistency and enable reasoning over the data.

The second key component is the data layer, which includes the sources and storage mechanisms. Knowledge graphs often aggregate data from diverse sources like databases, APIs, or unstructured text. For instance, a company might integrate customer data from a CRM with product details from an inventory database. To store this, graph databases (e.g., Neo4j) or triple stores (e.g., Apache Jena) are commonly used. Triples—structured as “subject-predicate-object” (e.g., “Apple-foundedBy-Steve Jobs”)—form the basic units of storage. Indexing and query languages like SPARQL or Cypher enable efficient retrieval, allowing developers to traverse connections (e.g., “Find all products manufactured by suppliers in Germany”).

Finally, applications and services leverage the graph for tasks like search, recommendation, or analytics. For example, a recommendation system might use relationship paths to suggest related products (“Users who bought X also bought Y”). Knowledge graphs also support semantic search by understanding context—searching for “Java” could return results about the island or programming language based on connected entities. Tools like RDF for data modeling or OWL for ontology creation help maintain the graph’s structure. By combining these components, developers can build systems that organize complex data and uncover insights through interconnected information.