Knowledge graphs can be stored in any back end, from files to relational databases or document stores. But since they are, well, graphs, it does make sense to store them in a graph database.

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

If CIOs want to start exploiting the hidden knowledge and untapped potential in their internal data stores by applying LLMs to them, then building and refining knowledge graphs using proven graph ...

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

Through natural language queries and graph-based RAG, TigerGraph CoPilot addresses the complex challenges of data analysis and the serious shortcomings of LLMs for business applications.

Since its launch in January of this year, Facebook has been rolling out their Graph Search, allowing a limited number of users to perform queries with the natural language interface. In a post on ...

Alongside text-based large language models (LLMs), including ChatGPT in industrial fields, GNN (Graph Neural Network)-based graph AI models that analyze unstructured data such as financial ...