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.

Imagine your database of choice blown out of the water by a startup emerging from stealth. TigerGraph may have done just that for graph databases.

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.

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.

Graph databases are making a splash in the database market, with specialist, multimodal and cloud database suppliers jostling for a slice of the pie.

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 ...

At Data Summit Connect 2020, Thomas Cook, director of sales, Cambridge Semantics, explained the basics of knowledge graphs and how they leverage natural-language processing to automate the ...

The Bulgarian graph database startup Graphwise today announced a major upgrade to its flagship GraphDB tool, adding new features aimed at boosting enterprise knowledge management and creating a ...