Neo4j ®, the leading graph database and analytics platform, today unveiled Infinigraph: a new distributed graph architecture now available in Neo4j's self-managed offering. Infinigraph enables Neo4j's ...

Neo4j also trumpeted the value of graphs as vector databases used in generative artificial intelligence. AI training requires ...

Lyft's "Amundsen" metadata system is an example of how knowledge graphs are spreading throughout companies with grass-roots projects. It's all part of winning hearts and minds, in the view of ...

You can think of a graph database as a set of interconnected circles (nodes) and each node represents a person, a product, a place or ‘thing’ that we want to build into our data universe.

Graph database query languages are growing, along with graph databases. They let developers ask complex questions and find relationships.

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

The big data revolution is generating a mess of unruly data that’s difficult to parse and understand. This is to be expected–explosions don’t generally occur in a nice, orderly fashion, after all. But ...

Knowledge Graphs are quickly being adopted because they have the advantages of linking and analyzing vast amounts of interconnected data. The promise of graph technology has been there for a decade.

Graph database startup Neo4j raised $320 million at an over $2 billion valuation, highlighting the value of graph databases.

Knowledge graphs are on the rise at enterprises that seek more effective ways to connect the dots between the data world and the business world. Paired with complementary AI technologies such as ...

Graph databases have always been useful to help find connections across a vast data set, and it turns out that capability is quite handy in artificial ...