We had a chance to speak with TigerGraph's incoming head of product R&D, and it spurred some thoughts on where we thought graph databases should go.

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.

Graph database vendor Neo4j announced today new capabilities for vector search within its graph database. Neo4j’s namesake database technology enables organizations to create a knowledge graph ...

This can make it quite slow as opposed to a graph database that is densely connected and easily queried. As sensors become more widely used in wearables such as Google Glass, the demand for graph ...

Newsela uses Dgraph, a “graph database,” to speed the delivery of content while making it easier for the company’s developers to create new features.

For the past few years, I've been trying to understand how to make graphs using RRDtool (Round-Robin Database tool) after failing miserably to understand MRTG (Multi-Router Traffic Grapher) before ...

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 ...

At Data Summit Connect 2020, Thomas Cook, director of sales, Cambridge Semantics, explained the basics of knowledge graphs and how they leverage natural-language processing to automate the ...