How materialczar50/semantica works
Semantica is graph-native knowledge and context infrastructure for AI systems. Its principal workflow takes enterprise data through ingestion and semantic extraction into queryable knowledge/context graphs, where ontology models, graph analytics, reasoning, and decision context support downstream applications. Provenance and audit trails make those results traceable. The Explorer provides a user-facing way to work with graphs and related capabilities; framework integrations expose Semantica to agent ecosystems. The README documents these capabilities, but sampled source is incomplete, so the graph avoids asserting detailed internal call paths.
Main entry points: knowledge_graph.py Knowledge Graph · context_graph.py Context Graph · ingest Data Ingestion · parse Document Parsing
Components 20
Data Preparation
- Data Ingestion
semantica/ingest - Document Parsing
semantica/parse - Semantic Extraction
semantica/semantic_extract - Data Normalization
semantica/normalize
Knowledge Modeling
- Ontology Management
semantica/ontology - Knowledge Graph
semantica/kg/knowledge_graph.py - Graph Storage
semantica/graph_store - RDF Storage
semantica/triplet_store - Graph Analytics
semantica/kg
Context and Decisions
- Context Graph
semantica/context/context_graph.py - Agent Memory
semantica/context/agent_memory.py - Deterministic Reasoning
semantica/reasoning/reasoner.py - Decision Intelligence
semantica/context/decision_recorder.py - Vector Search
semantica/vector_store
Access and Integrations
- Knowledge Explorer
semantica/explorer/app.py - Explorer Workspaces
explorer/src/App.tsx - Agent Integrations
integrations - MCP Tools
semantica_mcp/mcp/server.py
Traceability
- Provenance Tracking
semantica/provenance/manager.py
Other components
- Data and AI Teams
Connections 20
- Data and AI Teams → Data Ingestion: supply data
README.md - Data Ingestion → Document Parsing: ingest sources
- Document Parsing → Semantic Extraction: extract meaning
- Semantic Extraction → Data Normalization: prepare entities
- Data Normalization → Knowledge Graph: build graph
README.md - Ontology Management → Knowledge Graph: model meaning
README.md - Knowledge Graph → Graph Storage: store graph
README.md - Knowledge Graph → RDF Storage: store RDF
README.md - Knowledge Graph → Graph Analytics: analyze graph
README.md - Knowledge Graph → Context Graph: build context
README.md - Context Graph → Agent Memory: inform agent context
- Context Graph → Deterministic Reasoning: support reasoning
README.md - Context Graph → Decision Intelligence: inform decisions
README.md - Context Graph → Vector Search: enable retrieval
- Decision Intelligence → Provenance Tracking: record decision trail
README.md - Knowledge Graph → Provenance Tracking: trace graph lineage
README.md - Data and AI Teams → Explorer Workspaces: explore knowledge
README.md - Explorer Workspaces → Knowledge Explorer: access services
- Agent Integrations → Context Graph: use context
README.md - MCP Tools → Context Graph: expose graph tools