Knowledge Graphs
Knowledge Graphs
Knowledge graphs represent a powerful approach to organizing information in a way that captures not just data, but the relationships between different data points. Unlike traditional databases, knowledge graphs structure information as a network, with entities as nodes and relationships as connecting edges.
These sophisticated data structures enable machines to understand and process information in ways that more closely mimic human cognition. By connecting related concepts, knowledge graphs create context that allows for more intelligent data processing, inference, and discovery.

Network of Entities
The entities in the Google knowledge graph represent the world as we know it, marking a shift from “strings to things.” Behind this simple phrase is the profound concept of treating information on the web as entities rather than a bunch of text.
Since information is organized as a network of entities, Google can tap into the collective intelligence of the knowledge graph to return results tailored to the meaning of your query rather than a simple keyword match.

RDF Triples
RDF, which stands for Resource Description Framework, is a standardized method for expressing data in the form of a directed graph using subject-predicate-object statements, commonly referred to as “triples.”
The foundational unit of a knowledge graph is the triple. It comprises two nodes that represent entities connected by a single edge to articulate their relationship. Represented as “subject-predicate-object” statements, a triple illustrates how an entity (subject) links to another entity or a simple value (object) through a specific property (predicate).
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