Semantic Memory
Master how AI agents store and organize facts, knowledge, and concepts in structured semantic memory systems
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Storing knowledge is only half the battle—agents must retrieve relevant facts and reason over them to answer questions intelligently.
Retrieval involves searching the semantic memory for concepts matching a query. Reasoning goes further: inferring new knowledge by following relationships and applying logical rules.
Together, they enable agents to answer complex questions like "What frameworks are written in Python?" or"What skills do Data Scientists need?"
Interactive: Query a Knowledge Base
Try searching for concepts in our mini knowledge base. See how retrieval works.
🧠 Reasoning Through Inference
See how agents reason by chaining relationships to infer new knowledge.
🔍 Retrieval Strategies
- •Exact Match: Find concepts with exact name/ID
- •Keyword Search: Match partial text in descriptions
- •Semantic Search: Find conceptually similar entities
- •Graph Traversal: Follow relationships to related concepts
🧩 Reasoning Methods
- •Deduction: Apply rules to derive facts ("All mammals breathe air")
- •Induction: Generalize from examples ("Most birds fly")
- •Abduction: Best explanation for observations ("Grass is wet → it rained")
- •Transitive: Chain relationships (A→B, B→C ∴ A→C)
💻 Query Languages: SPARQL
SPARQL is a query language for RDF knowledge graphs, similar to SQL for databases. It allows pattern matching over triples.
💡 Key Insights
Retrieval: Finding what's explicitly stored. Reasoning: Deriving what's implicitly true.
Complex reasoning is powerful but slower. Balance depth with response time.