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Semantic Memory

Master how AI agents store and organize facts, knowledge, and concepts in structured semantic memory systems

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How to Organize Knowledge

Raw facts alone aren't useful—agents need structured knowledge to understand relationships, make inferences, and reason intelligently. This is where knowledge representation comes in.

Knowledge representation defines how we organize semantic memory, from simple flat lists to complex hierarchical structures. Let's explore the three main approaches.

Interactive: Knowledge Organization Structures

Compare different ways to organize knowledge, from simple lists to sophisticated ontologies.

📝

Flat List (Unstructured)

Simple collection of facts without relationships.

• Dog
• Cat
• Eagle
• Whale
• Python
• API
✅ Advantages
  • • Simple to implement
  • • Fast to search
  • • Easy to update
❌ Limitations
  • • No relationships
  • • No reasoning
  • • Limited intelligence
Tip: Most modern AI agents use ontologies for semantic memory, as they support complex reasoning and can model domain expertise with precision.

🏢 Real-World Example: Customer Support Agent

Flat List Approach:
• Product A
• Product B
• Warranty
• Returns

❌ Agent can't answer "Does Product A have a warranty?"

Ontology Approach:
Product A has-warranty → Standard Warranty
Standard Warranty has-duration → 1 year
Standard Warranty covers → Manufacturing Defects

✅ Agent can answer complex queries by traversing relationships

Schema

Template for organizing related concepts (e.g., "Restaurant" schema includes location, cuisine, hours)

Frame

Data structure with slots for attributes (e.g., Person frame with name, age, occupation slots)

Triple

Subject-Predicate-Object fact ("Paris" "is-capital-of" "France") used in knowledge graphs

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