A specialized database for storing and querying RDF triples — the building blocks of knowledge graphs.
A triple store (also called an RDF store) is a purpose-built database for storing and retrieving semantic data expressed as RDF triples — subject-predicate-object statements. Unlike relational databases optimized for tabular data or document databases optimized for JSON, triple stores are specifically designed for the graph-structured, semantically rich data that powers knowledge graphs and ontology-based applications.
Triple stores have experienced a renaissance in 2026 as knowledge graphs have moved from academic research to enterprise production. The ability to store and query semantic relationships at scale — answering questions like 'find all entities related to X within 3 hops that have property Y' — is essential for enterprise AI applications. Modern triple stores combine traditional SPARQL querying with vector search capabilities, creating hybrid systems optimized for AI workloads.
Triple stores index RDF triples in specialized data structures optimized for graph traversal and pattern matching. They support SPARQL (the standard query language for RDF data) and often provide additional features like named graphs (for managing multiple knowledge graphs), inference engines (for deriving new facts from existing ones), and federation (for querying across multiple triple stores). Leading implementations include Apache Jena, Stardog, GraphDB, and Amazon Neptune.
A pharmaceutical company stores their drug interaction knowledge graph in a triple store with 500 million triples. A SPARQL query can instantly find all drugs that interact with a given compound, all compounds that target a specific protein, and all clinical trials involving those compounds — traversing complex relationships that would require dozens of SQL joins in a relational database.