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

Google popularized the term "knowledge graph" in this 2012 blog post. Since then, there has been a massive momentum around ...

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

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

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 ...

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 ...

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 ...

Value of Connections Highly connected data enables or enhances several application areas, such as Entity 360°, Pattern Detection, Fraud Detection, Insider Threat Detection, and Data Unification.We ...

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.