To stay visible in AI search, your content must be machine-readable. Schema markup and knowledge graphs help you define what ...

Knowledge Graphs, by contrast, represent data as a network of nodes (entities) and edges (relationships). They can handle more complex, nuanced queries based on the types of connections, the ...

To keep an SLM relevant and accurate, you still need to feed it fresh, contextual data. That’s where graph technology comes ...

Knowledge graphs: The link between data and meaning While Google popularized the term “knowledge graph” in 2012, the concept of representing knowledge as interconnected information has roots ...

Banks, miners and police forces in Australia are among those using graph databases to provide the context and data relationships needed for more accurate and trustworthy AI, moving projects from exper ...

The journey from unstructured data (texts, images, etc.) to a fully structured knowledge graph—rich in facts, logical constraints, and recursive rules—is complex and challenging, but the ...

As industrial data grows in complexity and scale, traditional methods of managing information, focused on storage and basic ...

Google's Knowledge Graph pulls its data from a variety of sources — one of them being Wikidata, an open repository of information that's hosted by the same organization that hosts Wikipedia.