Vector databases are more suitable for tasks involving similarity and machine learning, while knowledge graph databases excel in modeling and querying interconnected, complex, semantically rich data.

The semantic technologies underpinning RDF knowledge graphs are primed for data mesh and data fabric architectures — and their synthesis. They’re certainly ideal for crafting data products.

As the saying goes, context is everything – and this is certainly the case with AI. To be useful, AI systems need to be able to understand nuance and deliver accurate, relevant results. Our ability to ...

Both graph databases and knowledge graphs “have similarities but serve different purposes,” said Shalvi Singh, senior product manager at Amazon AI. “Graph databases serve as the underlying ...

Graph database vendor Neo4j announced today new capabilities for vector search within its graph database.. Neo4j’s namesake database technology enables organizations to create a knowledge graph ...

Knowledge graphs are hyped. We can officially say this now, since Gartner included knowledge graphs in the 2018 hype cycle for emerging technologies. Though we did not have to wait for Gartner ...

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 ...

Franz Inc., an early innovator in Artificial Intelligence (AI) and leading supplier of Graph Database technology for Entity-Event Knowledge Graph Solutions, is introducing AllegroGraph 8.0, a ...

Knowledge graphs—machine-readable data representations that mimic human knowledge—are bridging the gap between proprietary enterprise data and safe, reliable, helpful LLMs.