What is RDF vs Property Graph?

Choosing the wrong graph model can lock an organisation into a technology that does not meet its long-term needs. RDF and property graphs have different strengths, and the choice should be driven by use cases, not vendor marketing.

Choose RDF when: You need to integrate data across organisational boundaries, comply with industry ontology standards (FIBO, DCAT, SNOMED CT), enable formal reasoning, or publish linked data.

Choose property graphs when: You are building an application-specific graph (recommendations, fraud detection), your team prefers imperative query styles, and you do not need cross-organisational interoperability.

Many organisations end up using both - RDF for the enterprise semantic layer and property graphs for high-performance application workloads - with data flowing between them.

Data model: RDF stores data as subject-predicate-object triples with URI identifiers. Property graphs store nodes and edges, both of which can carry key-value properties.

Schema: RDF uses OWL and RDFS for formal ontologies with reasoning. Property graphs typically use application-level schema or optional constraints (e.g., Neo4j constraints).

Query language: RDF uses SPARQL (W3C standard, declarative, supports federation). Property graphs use Cypher (Neo4j), Gremlin (Apache TinkerPop), or the emerging GQL standard.

Interoperability: RDF’s URI-based identifiers and W3C standards make it inherently interoperable. Property graphs are typically silo’d within a single database instance.

Reasoning: RDF supports OWL reasoning natively. Property graphs require custom application logic for inference.

Developer experience: Property graphs are often perceived as easier for developers familiar with object-oriented programming. RDF has a steeper learning curve but offers more expressive power.

RDF in practice: A European bank uses an RDF knowledge graph aligned with FIBO to harmonise entity data across 14 legacy systems. The W3C standards ensure that data from different jurisdictions can be merged without naming collisions.

Property graph in practice: A fintech startup uses Neo4j to power a real-time fraud detection engine. The property graph model makes it easy to traverse payment chains and score risk at transaction time.

Hybrid approach: A pharmaceutical company maintains an RDF knowledge graph for regulatory and scientific data integration, and feeds curated subsets into a Neo4j graph for a researcher-facing application with a custom Cypher query interface.