Digitise any domain model we want The long arc of business IT history points to more applications and more use cases being accepted as great fits for graph databases or graph data science (GDS).

Data-hungry AI applications are fed complex information, and that's where graph databases and knowledge graphs play a crucial role.

By combining ontology and large language model-driven techniques, engineers can build a knowledge graph that is easily queried and updatable.

Real-time database vendor Aerospike is expanding its multi-model capabilities with the launch of the Aerospike Graph database. Aerospike got its start back in 2009, providing a NoSQL database that ...

The intersection of large language models and graph databases is one that’s rich with possibilities. The folks at property graph database maker Neo4j today took a first step in realizing those ...

The new database adds a property graph data model to the existing capabilities of its NoSQL Database and Apache TinkerPop graph compute engine.

Banks, miners and police forces in Australia are among those using graph databases to provide the context and data relationships needed for more accurate and trustworthy AI, moving projects from ...

Graph database startup TigerGraph Inc. today announced a major update to its flagship cloud platform with the Savanna release, bringing with it six times faster network deployments and dozens of ...