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🕸️
Semantic Memory Networks(SMN)
General world knowledge systems divorced from specific acquisition context, supporting factual knowledge and concept relationships for multi-agent agentic AI systems
Complexity: highMemory Management
🎯 30-Second Overview
Pattern: General world knowledge systems divorced from acquisition context, supporting factual reasoning
Why: Context-independent knowledge access, cross-domain reasoning, consistent facts across agents
Key Insight: Concepts + Relationships + Embeddings → Multi-hop reasoning → Cross-agent knowledge consistency
⚡ Quick Implementation
1Build Graph:Create knowledge graph with concepts & relationships
2Embed Concepts:Generate semantic embeddings for all entities
3Link Relations:Map hierarchical & associative connections
4Share Network:Enable cross-agent knowledge graph access
5Update Graph:Continuously refine relationships & add concepts
Example: concept_extraction → embedding_generation → relationship_mapping → cross_agent_sharing → continuous_refinement
📋 Do's & Don'ts
✅Use hierarchical concept organization (parent-child relationships)
✅Implement semantic similarity scoring for concept retrieval
✅Enable multi-hop reasoning across concept relationships
✅Maintain consistent ontologies across all agents in the network
✅Version control knowledge graphs for reproducible reasoning
❌Store time-sensitive or rapidly changing information
❌Create overly complex relationship hierarchies (keep interpretable)
❌Ignore semantic consistency when merging knowledge from different sources
❌Let knowledge graphs grow without pruning irrelevant connections
❌Use for personal or context-specific information storage
🚦 When to Use
Use When
- • Complex domain knowledge representation
- • Multi-hop reasoning required
- • Cross-domain knowledge integration
- • Factual consistency across agents
- • Scientific/technical knowledge bases
Avoid When
- • Simple key-value data storage
- • Highly personal/contextual information
- • Rapidly changing temporal data
- • Privacy-sensitive knowledge
- • Storage-constrained environments
📊 Key Metrics
Concept Coverage
% domain concepts represented
Relationship Accuracy
Correct semantic connections %
Query Resolution Rate
Successful knowledge retrieval %
Cross-Agent Consistency
Knowledge alignment score
Reasoning Path Length
Average hops to find answers
Knowledge Graph Density
Connections per concept ratio
💡 Top Use Cases
Scientific Research Networks: Physics, chemistry, biology concepts linked across disciplines (enables cross-domain discovery)
Medical Knowledge Systems: Diseases, symptoms, treatments, drug interactions in semantic network (diagnostic reasoning)
Legal Knowledge Graphs: Laws, precedents, regulations, jurisdiction relationships (legal research & compliance)
Financial Domain Models: Markets, instruments, regulations, risk factors interconnected (investment analysis)
Technical Documentation: Software concepts, APIs, dependencies, best practices linked (developer assistance)
References & Further Reading
Deepen your understanding with these curated resources
Academic Papers
Contribute to this collection
Know a great resource? Submit a pull request to add it.
🕸️
Semantic Memory Networks(SMN)
General world knowledge systems divorced from specific acquisition context, supporting factual knowledge and concept relationships for multi-agent agentic AI systems
Complexity: highMemory Management
🎯 30-Second Overview
Pattern: General world knowledge systems divorced from acquisition context, supporting factual reasoning
Why: Context-independent knowledge access, cross-domain reasoning, consistent facts across agents
Key Insight: Concepts + Relationships + Embeddings → Multi-hop reasoning → Cross-agent knowledge consistency
⚡ Quick Implementation
1Build Graph:Create knowledge graph with concepts & relationships
2Embed Concepts:Generate semantic embeddings for all entities
3Link Relations:Map hierarchical & associative connections
4Share Network:Enable cross-agent knowledge graph access
5Update Graph:Continuously refine relationships & add concepts
Example: concept_extraction → embedding_generation → relationship_mapping → cross_agent_sharing → continuous_refinement
📋 Do's & Don'ts
✅Use hierarchical concept organization (parent-child relationships)
✅Implement semantic similarity scoring for concept retrieval
✅Enable multi-hop reasoning across concept relationships
✅Maintain consistent ontologies across all agents in the network
✅Version control knowledge graphs for reproducible reasoning
❌Store time-sensitive or rapidly changing information
❌Create overly complex relationship hierarchies (keep interpretable)
❌Ignore semantic consistency when merging knowledge from different sources
❌Let knowledge graphs grow without pruning irrelevant connections
❌Use for personal or context-specific information storage
🚦 When to Use
Use When
- • Complex domain knowledge representation
- • Multi-hop reasoning required
- • Cross-domain knowledge integration
- • Factual consistency across agents
- • Scientific/technical knowledge bases
Avoid When
- • Simple key-value data storage
- • Highly personal/contextual information
- • Rapidly changing temporal data
- • Privacy-sensitive knowledge
- • Storage-constrained environments
📊 Key Metrics
Concept Coverage
% domain concepts represented
Relationship Accuracy
Correct semantic connections %
Query Resolution Rate
Successful knowledge retrieval %
Cross-Agent Consistency
Knowledge alignment score
Reasoning Path Length
Average hops to find answers
Knowledge Graph Density
Connections per concept ratio
💡 Top Use Cases
Scientific Research Networks: Physics, chemistry, biology concepts linked across disciplines (enables cross-domain discovery)
Medical Knowledge Systems: Diseases, symptoms, treatments, drug interactions in semantic network (diagnostic reasoning)
Legal Knowledge Graphs: Laws, precedents, regulations, jurisdiction relationships (legal research & compliance)
Financial Domain Models: Markets, instruments, regulations, risk factors interconnected (investment analysis)
Technical Documentation: Software concepts, APIs, dependencies, best practices linked (developer assistance)
References & Further Reading
Deepen your understanding with these curated resources
Academic Papers
Contribute to this collection
Know a great resource? Submit a pull request to add it.