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

When enhanced by the rich, self-describing nature of semantic knowledge graphs, data mesh and data fabric can greatly complement one another.

Think of knowledge-graph-powered data catalogs as the search engine for the data in the enterprise.

Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks.

In the new knowledge-based digital world, encoding and making use of business and operational knowledge is the key to making progress and staying competitive. Here's a shortlist of technologies ...

Ever since the introduction of the Google Knowledge Graph, a growing number of organizations have adopted this powerful technology to drive efficiency and effectiveness in their data management ...

Understand the building blocks of knowledge graphs – entities, relationships and attributes – and how they relate to information retrieval.

Graph data science is when you want to answer questions, not just with your data, but with the connections between your data points — that’s the 30-second explanation, according to Alicia Frame.

The "Graph Item Type" does not default to "AREA", so be sure to select that for a traditional graph that looks like a rolling hill of data. It's safe to leave "Consolidation Function" to AVERAGE, and ...