When to choose Knowledge Graphs vs. Vector DBs. Specific use cases where Vector DBs excel are in RAG systems designed to assist customer service representatives.

Knowledge graphs excel in capturing knowledge explicitly, in a top-down way. Knowledge graphs are part of AI, the so-called good old AI, or symbolic AI.

Graph databases, like Neo4j, excel in managing data that’s all about connections. They store information as entities and the links between them, making it easier to see how everything is related.

Using the Graph API to work with Excel is relatively easy; it’s a set of REST APIs with a common structure for all calls. This lets you quickly build URLs that access OneDrive locations, which ...

At a time when every enterprise looks to leverage generative artificial intelligence, data sites are turning their attention to graph databases and knowledge graphs. The global graph database market ...

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.

Knowledge Graphs can be hard at first, but don't give up. Practice makes perfect. With the big data explosion, and the rise of NoSQL, something else started happening, too.

The graph of knowledge vs. the knowledge graph: GoK is a broader, more conceptual idea focusing on interconnected information, without necessarily being highly structured.

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 ...