What is an RDF graph?

An RDF (Resource Description Framework) graph is a method for representing data as a network of interconnected nodes and edges, designed to describe resources on the web. At its core, an RDF graph consists of triples: statements that link a subject, predicate, and object. For example, a triple might state “Book123 hasAuthor Alice” or “Alice worksAt CompanyX.” Each element in the triple is identified using URIs (Uniform Resource Identifiers) to ensure uniqueness, and the graph structure allows data to be connected across different sources. This approach is foundational for semantic web technologies, enabling data integration and interoperability.

The structure of an RDF graph revolves around three components. The subject is the resource being described (e.g., a book, person, or concept), the predicate defines the relationship or property (e.g., “hasAuthor” or “worksAt”), and the object is the value or another resource linked to the subject. For instance, using URIs, a subject like <http://example.org/Book123> could be linked via a predicate <http://example.org/hasAuthor> to an object <http://example.org/Alice>. Objects can also be literals, such as strings or numbers (e.g., "Alice" or 42), or blank nodes (anonymous resources without a URI). This flexibility allows RDF to model complex relationships, such as hierarchies, taxonomies, or metadata, in a standardized way.

RDF graphs are particularly useful in scenarios requiring data integration or semantic reasoning. For example, a developer might use RDF to merge product data from multiple e-commerce platforms by mapping their schemas to a common vocabulary. Tools like SPARQL (a query language for RDF) enable querying across these graphs, such as finding all products priced under $100 with a specific feature. RDF’s standardized format (serialized as Turtle, JSON-LD, or RDF/XML) also simplifies sharing data between systems. By representing data as a graph, RDF avoids the rigid structures of relational databases, making it easier to adapt to evolving schemas or heterogeneous datasets while maintaining clarity and traceability.