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

Context, not content, now drives AI visibility, making structured data the strategic data layer every enterprise must ...

Today's enterprises need unified semantic layers that seamlessly connect traditional BI, GenBI applications, and emerging AI workloads. As organizations face challenges in making enterprise data truly ...

DevRev, an AI-native enterprise software company focused on transforming how teams and customers collaborate, today announces ...

The chatbot leverages Stardog’s pioneering knowledge graph platform, which is a flexible and reusable data layer that can ...

The enterprise knowledge graph is a knowledge representation system based on graph structures. It integrates multi-source data from both internal and external sources (such as business information, ...

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 ...

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...

Jim Morris, solution engineer, Progress, and Stephen Reed, senior account manager, Progress, joined DBTA's webinar, Building Knowledge Graphs to Power Your AI Initiatives, to examine how knowledge ...

Neo4j ®, the leading graph database and analytics platform, today unveiled Infinigraph: a new distributed graph architecture now available in Neo4j's self-managed offering. Infinigraph enables Neo4j's ...

Neo4j also trumpeted the value of graphs as vector databases used in generative artificial intelligence. AI training requires ...