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.

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

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.

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.

Startups like TigerGraph, MongoDB, Cambridge Semantics, DataStax, and others compete with Neo4j in a graph database market expected to be worth $2.4 billion by 2023, in addition to incumbents like ...

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

The next challenge in data management is accessing data resources that are dispersed across a hybrid computing environment. Companies have invested in master data management solutions, breaking ...