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

The addition of vectors provides context to the graph database for enhanced search and supports generative AI and large language models.

Banks, miners and police forces in Australia are among those using graph databases to provide the context and data relationships needed for more accurate and trustworthy AI, moving projects from ...

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 ...

Graph database startup TigerGraph Inc. today announced a major update to its flagship cloud platform with the Savanna release, bringing with it six times faster network deployments and dozens of ...

With the rapid development of artificial intelligence (AI) technology, the graph database market is experiencing unprecedented growth, with an annual growth rate approaching 25%. Graph databases are ...

The rise of graph databases is closely related to AI's demand for data processing. AI technology requires vast amounts of structured and unstructured data, which must not only be input into ...

Although databases that focus on the relational aspect of data analytics abound, few are as effective at revealing the hidden valuable insights as a graph database.

A graph database is a type of database designed according to a graph, network, or link-based structure rather than traditional rows and columns. Its data is organized into interconnected nodes and ...

Graph databases are beneficial for applications like knowledge graphs, recommendation systems, and semantic search engines because they are excellent at capturing semantic context.