Knowledge Graphs

Also known as: Semantic Networks, Entity Graphs

A structured representation of knowledge that connects entities, their attributes, and relationships in a graph format to enable more intelligent data processing.

What is Knowledge Graphs?

Knowledge Graphs are structured data representations that organize information as a network of entities and their interrelationships. They provide a way to store and connect complex information in a format that both humans and machines can understand.

At their core, knowledge graphs consist of:

Nodes: Representing entities (people, places, concepts, etc.)
Edges: Representing relationships between entities
Properties: Attributes that describe entities

This structure allows knowledge graphs to capture semantic relationships and context in a way that traditional databases cannot. By connecting related concepts, knowledge graphs enable more sophisticated reasoning and inference capabilities.

Knowledge graphs power many modern AI systems, including:

Search engines that understand the relationships between search terms
Recommendation systems that connect related products or content
Virtual assistants that can answer complex questions by traversing connected information
Content management systems that automatically organize and link related information

Notable examples include Google's Knowledge Graph, which enhances search results with structured information, and enterprise knowledge graphs that connect organizational data across silos.

Related Terms

Structured Data

Information organized in a formatted, machine-readable way that helps search engines and AI systems understand the content and context of web pages.

Structured Data

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  "description": "A structured representation of knowledge that connects entities, their attributes, and relationships in a graph format to enable more intelligent data processing.",
  "inDefinedTermSet": {
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    "name": "AI Optimization Glossary",
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}

Term Details

Category: Structured Data
Type: concept
GEO Readiness: structured