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.

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.

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 ...

DZD, the German Federal Diabetes Research Centre, is using a Neo4j graph database to link up Covid-19 scientific research and scientists.

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

Knowledge graphs present a major business opportunity. The global data analytics market has a current compound annual growth rate of 28.9%, according to market research, and is expected to reach a ...

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 ...