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 databases offer a more efficient way to model relationships and networks than relational (SQL) databases or other kinds of NoSQL databases (document, wide column, and so on). Lately many ...

The goal of this type of database is to make it easier to discover and explore the relationships in a property graph with index-free adjacency using nodes, edges, and properties.

By combining ontology and large language model-driven techniques, engineers can build a knowledge graph that is easily queried and updatable.

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.

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 ...

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

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.

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 ...