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

Graph databases work best when the data you’re working with is highly connected and should be represented by how it links or refers to other data, typically by way of many-to-many relationships.

Fluree touts itself as the Web3 Data Platform -- a semantic graph database that guarantees data integrity, facilitates secure data sharing, and powers connected data insights, all in one pluggable ...

A new generation of graph databases has taken hold, and a generation of query languages has arrived alongside them. The assorted graph database query languages include the likes of Gremlin, Cypher ...

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.

AnzoGraph DB: Driving AI and Machine Insights with Knowledge Graphs in a Connected World Knowledge Graphs are quickly being adopted because they have the advantages of linking and analyzing vast ...

Graph databases are organized around relationships and make connections across a wide range of data types and formats to be viewed in a connected data map. Gartner predicts that graph technology will ...

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 ...