What is the difference between a graph database and a knowledge graph?

A graph database is a technology designed to store and manage data structured as a graph, consisting of nodes (representing entities) and edges (representing relationships between them). It’s optimized for querying interconnected data efficiently, using traversal operations to navigate relationships. Examples include Neo4j, Amazon Neptune, and JanusGraph. In contrast, a knowledge graph is a specific application of a graph structure that organizes real-world information into a network of entities and their semantic relationships, often enriched with contextual details. For instance, Google’s Knowledge Graph powers search results by linking people, places, and concepts. While graph databases provide the infrastructure, knowledge graphs represent the data model and content built on top of such infrastructure.

The primary distinction lies in their purpose and scope. A graph database is a general-purpose storage system that can handle any domain’s connected data, offering tools and query languages (e.g., Cypher, Gremlin) to create, update, and traverse graphs. It’s ideal for scenarios requiring frequent relationship-based queries, like social networks or fraud detection. A knowledge graph, however, is a curated dataset focused on capturing domain-specific knowledge with contextual meaning. It often incorporates ontologies (formal definitions of entity types and relationships) and may integrate data from multiple sources. For example, a medical knowledge graph might link diseases, symptoms, and treatments, enabling applications like diagnostic assistance. While knowledge graphs often use graph databases for storage, they can also be implemented with other technologies, such as RDF triplestores.

Practical examples highlight their differences. A developer might use Neo4j (a graph database) to build a recommendation engine for an e-commerce platform, leveraging its ability to quickly traverse user-product interactions. The same developer could later structure a subset of this data into a knowledge graph to formalize product categories, supplier relationships, and user preferences, enabling semantic search features. Another example is Wikidata, a publicly available knowledge graph that stores structured data about millions of entities, which might be queried using SPARQL (an RDF query language) rather than a traditional graph database query tool. In essence, graph databases are the “engine” for managing connected data, while knowledge graphs are the “maps” built using that engine to model specific domains.