semantica/docs/use-cases.md

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Use Cases	Real-world applications of Semantica across domains, with linked cookbook notebooks for each.	briefcase

Semantica is purpose-built for environments where AI outputs must be explainable, auditable, and traceable. The use cases below span regulated industries, advanced research, and high-stakes operational domains — each with linked Jupyter notebooks you can run today.

At a Glance

Use Case	Domain	Difficulty	Estimated Time
Biomedical Knowledge Graphs	Healthcare	Intermediate	1–2 hours
Financial Data Integration	Finance	Intermediate	1–2 hours
Fraud Detection	Finance	Advanced	2–3 hours
Blockchain Analytics	Finance	Intermediate	1–2 hours
Cybersecurity Threat Intelligence	Security	Advanced	2–3 hours
Criminal Network Analysis	Security / Intelligence	Intermediate	1–2 hours
Intelligence Analysis Orchestrator	Intelligence	Intermediate	1–2 hours
Supply Chain Optimization	Operations	Intermediate	1–2 hours
Renewable Energy Management	Energy	Intermediate	1–2 hours
GraphRAG	AI / LLM	Advanced	1–2 hours

Difficulty levels:

• Beginner — basic Semantica knowledge only, no domain expertise needed
• Intermediate — some domain knowledge helpful, uses 2–4 Semantica modules
• Advanced — domain expertise expected, uses advanced features (temporal graphs, multi-source pipelines, reasoning)

Research & Science

Biomedical Knowledge Graphs

Connect genes, proteins, drugs, and diseases from scientific literature and databases to accelerate drug discovery and understand disease mechanisms. Semantica's temporal graphs and provenance tracking make every fact in the knowledge base traceable to its source publication.

Key modules: ingest (PubMed RSS), semantic_extract, kg, deduplication, context

Cookbooks:

• Drug Discovery Pipeline — PubMed RSS ingestion, entity-aware chunking, GraphRAG, vector similarity search
• Genomic Variant Analysis — bioRxiv RSS, temporal KGs, deduplication, pathway analysis

Finance & Trading

Financial Data Integration

Unify financial data from APIs, MCP servers, and real-time streams into a single queryable knowledge graph — with conflict detection when sources disagree and full provenance back to each data feed.

Key modules: ingest (API, MCP, stream), normalize, kg, conflicts, provenance

Cookbook: Financial Data Integration (MCP) — Alpha Vantage API, MCP servers, seed data, real-time ingestion

Fraud Detection

Detect complex fraud rings using temporal graphs and pattern detection over transaction, device, and user data. Temporal edges let you query: "what connections existed during this window?" — critical for reconstructing fraud timelines.

Key modules: kg (temporal), conflicts, reasoning, visualization

Cookbook: Fraud Detection — temporal KGs, cycle detection, fraud pattern analysis

Blockchain Analytics

Map transaction flows, analyze DeFi protocols, and detect illicit activity across wallet and exchange networks. Graph algorithms (centrality, community detection) surface high-risk actors that linear transaction analysis misses.

Cookbooks:

• DeFi Protocol Intelligence
• Transaction Network Analysis

Security & Intelligence

Cybersecurity Threat Intelligence

Ingest threat feeds (CVE databases, security RSS), detect anomalies in streaming data, and build threat intelligence knowledge graphs for proactive defense. Real-time streaming ingestion with temporal provenance means every threat event is timestamped and traceable.

Key modules: ingest (stream, feed), kg (temporal), context, reasoning, export

Cookbooks:

• Real-Time Anomaly Detection
• Threat Intelligence Hybrid RAG

Criminal Network Analysis

Build knowledge graphs from police reports, court records, and OSINT feeds to identify key players, communities, and suspicious patterns. Network centrality analysis (PageRank, betweenness) surfaces actors that text search alone would miss.

Key modules: ingest, semantic_extract, kg, visualization (community detection)

Cookbook: Criminal Network Analysis

Intelligence Analysis Orchestrator

Process multiple intelligence sources in parallel using an orchestrator-worker pipeline pattern with multi-source conflict detection and resolution. When sources disagree on the same fact, Semantica flags and resolves rather than silently discarding.

Key modules: pipeline, ingest, conflicts, provenance, export

Cookbook: Intelligence Analysis Orchestrator-Worker

Industry & Operations

Supply Chain Optimization

Map suppliers, logistics routes, inventory levels, and delivery relationships to identify bottlenecks and optimize global supply chains. Graph path-finding reveals indirect dependencies that spreadsheet analysis cannot.

Key modules: ingest, kg, reasoning, visualization, export (Parquet for analytics)

Cookbook: Supply Chain Data Integration

Renewable Energy Management

Connect sensor data, weather forecasts, and maintenance logs to predict equipment failures and optimize grid operations. Temporal graphs let you track asset states over time and correlate maintenance events with performance degradation.

Key modules: ingest (stream, API), kg (temporal), reasoning, visualization

Cookbook: Energy Market Analysis

Advanced AI Patterns

GraphRAG (Graph-Augmented Generation)

Use knowledge graphs to retrieve precise, structured context for LLM responses — with hybrid retrieval (vector + graph traversal), logical inference, and source attribution on every claim. Every answer links back to a node in the graph, making hallucination auditable rather than invisible.

Key modules: context, vector_store, kg, reasoning, llms

Cookbooks:

• GraphRAG Complete — production-ready implementation with hybrid retrieval
• RAG vs. GraphRAG Comparison — side-by-side benchmark on real-world data

Full notebook catalog organized by topic and difficulty. Every module with code examples. Complete technical documentation. Have a use case to add? [Open a PR](https://github.com/semantica-agi/semantica) or start a discussion on GitHub.