Semantic Memory
Master how AI agents store and organize facts, knowledge, and concepts in structured semantic memory systems
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0 / 5 completedKnowledge Graphs & Semantic Networks
While taxonomies organize knowledge hierarchically, semantic networks (also called knowledge graphs) create a web of interconnected concepts with rich, labeled relationships.
Think of them as a map of knowledge: nodes represent concepts (entities), and edges represent typed relationships between them. This structure enables agents to "walk" through knowledge, discovering facts and making inferences.
Knowledge graphs power systems like Google's Search, recommendation engines, and AI assistants.
Interactive: Explore a Knowledge Graph
Click on a concept to see its relationships. Toggle relationship visibility to understand the network structure.
Selected Concept
📐 The Triple Format (Subject-Predicate-Object)
Knowledge graphs store facts as triples: a subject, a predicate (relationship), and an object. This simple structure is powerful and machine-readable.
🔍 Google Knowledge Graph
When you search "Who created Python?", Google doesn't just match keywords—it traverses its knowledge graph to find the created-by relationship.
🎬 Recommendation Systems
Netflix/Spotify use knowledge graphs to connect users, content, genres, and preferences for personalized recommendations.
✨ Why Knowledge Graphs Excel
Support diverse relationship types (is-a, part-of, created-by, used-for, etc.)
Easily add new concepts and relationships without restructuring
Standard formats (RDF, OWL) enable automated reasoning
Traverse relationships to discover implicit knowledge