How does a knowledge graph differ from a traditional database?

A knowledge graph differs from a traditional database primarily in how data is structured, queried, and used to represent relationships. Traditional databases, like relational databases, store data in tables with predefined schemas, where rows represent records and columns define attributes. Relationships between tables are established using foreign keys, which require explicit joins during queries. In contrast, a knowledge graph represents data as a network of interconnected entities (nodes) and their relationships (edges). Each node and edge can have properties, and relationships are treated as first-class citizens, enabling direct traversal of connections without complex joins. This structure allows knowledge graphs to model real-world relationships more naturally.

For example, in a relational database, you might have a Customer table and an Order table linked by a customer_id foreign key. To find all orders from a customer in a specific city, you would write a SQL query joining the tables. In a knowledge graph, the same data could be represented as a Customer node connected to Order nodes via placed_order edges, with properties like city directly attached to the Customer node. Querying this with a graph query language (e.g., SPARQL or Cypher) would involve traversing the edges directly, which can be more intuitive for interconnected data. Additionally, knowledge graphs often support inferencing—like automatically detecting that two customers live in the same city through shared relationships—which isn’t a native feature of traditional databases.

The flexibility of knowledge graphs makes them better suited for scenarios where relationships and context are critical. Traditional databases excel at transactional operations and structured data with fixed schemas, such as inventory management or financial records. Knowledge graphs, however, thrive in applications like recommendation systems (e.g., suggesting products based on user behavior patterns), semantic search (e.g., understanding that “Apple” refers to a company or fruit based on connected entities), or fraud detection (identifying suspicious connections between accounts). They also adapt more easily to evolving data models, as adding new relationship types or properties doesn’t require schema migrations. This makes knowledge graphs ideal for integrating heterogeneous data sources while preserving context.