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-# Semantica Domain Use Cases Catalog
-
-This document provides a detailed overview of the various domain-specific use cases and pipelines implemented using the Semantica framework. These examples demonstrate how Semantica can be applied to complex real-world problems across different industries.
-
-## Cookbook Notebooks
-
-**Selected Use Cases**: Two use cases per domain have been selected for comprehensive cookbook implementation (18 total cookbooks). These cookbooks demonstrate:
-- **Unique problem-solving approaches** tailored to each specific use case
-- **Relevant Semantica capabilities** (ingestion, parsing, extraction, knowledge graphs, embeddings, vector stores, graph analytics, reasoning, visualization, export)
-- **Domain-specific entity types, relationships, and ontologies**
-- **End-to-end pipelines** from data ingestion to insights and visualization
-
-**Note**: Each cookbook is uniquely designed for its use case - no cookie-cutter templates. Different use cases emphasize different Semantica capabilities (vector-focused, graph-focused, temporal-focused, stream-focused, hybrid RAG, etc.)
-
-### Selected Cookbooks by Domain
-
-- **Biomedical**: Drug Discovery Pipeline, Genomic Variant Analysis
-- **Blockchain**: DeFi Protocol Intelligence, Transaction Network Analysis
-- **Cybersecurity**: Real-Time Anomaly Detection, Threat Intelligence Hybrid RAG
-- **Finance**: Financial Data Integration (MCP), Fraud Detection
-- **Healthcare**: Clinical Reports Processing, Drug Interactions Analysis
-- **Intelligence & Law Enforcement**: Criminal Network Analysis, Intelligence Analysis Orchestrator-Worker
-- **Renewable Energy**: Energy Market Analysis, Smart Grid Management
-- **Supply Chain**: Supply Chain Data Integration, Supply Chain Risk Management
-- **Trading**: Risk Assessment, News Sentiment Analysis
-
----
-
-## **Biomedical**
-
-### **1. Drug Discovery Pipeline**
-- **Overview**: A complete drug discovery pipeline that ingests drug and protein data, extracts compound and target entities, builds a drug-target knowledge graph, and performs similarity search to predict interactions. Showcases vector embeddings and similarity search capabilities.
-- **Key Features**: Compound-target extraction, vector similarity search, interaction prediction, embedding generation, target identification.
-- **Pipeline**: `Drug/Protein Data Sources → Parse → Extract Entities → Build Drug-Target KG → Generate Embeddings → Vector Store → Similarity Search → Predict Interactions → Target Identification`.
-- **Cookbook**: [:notebook: Drug Discovery Pipeline](biomedical/01_Drug_Discovery_Pipeline.ipynb)
-
-### **2. Genomic Variant Analysis**
-- **Overview**: Analyzes genomic data to extract variant entities, build temporal genomic knowledge graphs, and analyze disease associations through pathway analysis. Demonstrates graph analytics, reasoning, and temporal analysis.
-- **Key Features**: Variant impact prediction, disease association analysis, pathway analysis, graph reasoning, temporal genomic KGs.
-- **Pipeline**: `Genomic Data Sources → Parse → Extract Entities → Build Temporal Genomic KG → Graph Analytics → Analyze Associations → Reasoning → Predict Impact → Pathway Analysis`.
-- **Cookbook**: [:notebook: Genomic Variant Analysis](biomedical/02_Genomic_Variant_Analysis.ipynb)
-
----
-
-## **Blockchain**
-
-### **1. DeFi Protocol Intelligence**
-- **Overview**: Ingests DeFi data to extract protocol entities, build DeFi knowledge graphs, and assess risks or optimize yields.
-- **Key Features**: Protocol risk assessment, yield optimization, relationship analysis.
-- **Pipeline**: `DeFi Data Sources → Parse → Extract Entities → Build DeFi KG → Analyze Relationships → Risk Assessment → Yield Optimization`.
-- **Cookbook**: [:notebook: DeFi Protocol Intelligence](blockchain/01_DeFi_Protocol_Intelligence.ipynb)
-
-### **2. Transaction Network Analysis**
-- **Overview**: Analyzes blockchain transaction networks to detect patterns, identify whale movements, and analyze token flows.
-- **Key Features**: Transaction pattern detection, whale tracking, flow analysis.
-- **Cookbook**: [:notebook: Transaction Network Analysis](blockchain/02_Transaction_Network_Analysis.ipynb)
-
----
-
-## **Cybersecurity**
-
-### **1. Real-Time Anomaly Detection**
-- **Overview**: Streams security logs in real-time to build temporal knowledge graphs and detect anomalies using pattern detection.
-- **Key Features**: Real-time log parsing, temporal pattern detection, automated alerting.
-- **Pipeline**: `Stream Security Logs → Real-Time Parsing → Extract Entities → Build Temporal KG → Pattern Detection → Anomaly Detection`.
-- **Cookbook**: [:notebook: Real-Time Anomaly Detection](cybersecurity/01_Real_Time_Anomaly_Detection.ipynb)
-
-### **2. Threat Intelligence Hybrid RAG**
-- **Overview**: Combines vector search with temporal knowledge graphs for advanced threat intelligence querying.
-- **Key Features**: Vector + KG hybrid search, context-aware retrieval.
-- **Cookbook**: [:notebook: Threat Intelligence Hybrid RAG](cybersecurity/02_Threat_Intelligence_Hybrid_RAG.ipynb)
-
----
-
-## **Finance**
-
-### **1. Financial Data Integration (MCP)**
-- **Overview**: Integrates Python/FastMCP servers to ingest real-time market data, stock prices, and financial metrics into a comprehensive financial knowledge graph. Demonstrates MCP server integration for live data streaming.
-- **Key Features**: MCP integration, real-time market data ingestion, multi-source financial KG construction, API-based data streaming.
-- **Pipeline**: `MCP Servers → Real-Time Data Ingestion → Parse Market Data → Extract Financial Entities → Build Financial KG → Real-Time Updates`.
-- **Cookbook**: [:notebook: Financial Data Integration (MCP)](finance/01_Financial_Data_Integration_MCP.ipynb)
-
-### **2. Fraud Detection**
-- **Overview**: Analyzes transaction streams using temporal knowledge graphs to detect fraud patterns and anomalies in real-time. Demonstrates stream processing, temporal graph construction, and pattern-based anomaly detection.
-- **Key Features**: Temporal transaction KGs, real-time anomaly detection, pattern recognition, automated alerting, stream processing.
-- **Pipeline**: `Transaction Streams → Real-Time Parsing → Build Temporal KG → Pattern Detection → Anomaly Detection → Real-Time Alerts`.
-- **Cookbook**: [:notebook: Fraud Detection](finance/02_Fraud_Detection.ipynb)
-
----
-
-## **Healthcare**
-
-### **1. Clinical Reports Processing**
-- **Overview**: Processes EHR systems and HL7/FHIR APIs to build patient knowledge graphs and store them in triplet stores.
-- **Key Features**: EHR integration, medical entity extraction, triplet store storage.
-- **Cookbook**: [:notebook: Clinical Reports Processing](healthcare/01_Clinical_Reports_Processing.ipynb)
-
-### **2. Drug Interactions Analysis**
-- **Overview**: Analyzes FDA databases and medical literature to detect drug-drug and drug-condition interactions.
-- **Key Features**: Interaction detection, safety ontology generation, drug knowledge graph.
-- **Cookbook**: [:notebook: Drug Interactions Analysis](healthcare/02_Drug_Interactions_Analysis.ipynb)
-
----
-
-## **Intelligence & Law Enforcement**
-
-### **1. Criminal Network Analysis**
-- **Overview**: Processes police reports and court records to build knowledge graphs for analyzing criminal networks and relationships.
-- **Key Features**: Relationship analysis, network centrality, intelligence reporting.
-- **Cookbook**: [:notebook: Criminal Network Analysis](intelligence/01_Criminal_Network_Analysis.ipynb)
-
-### **2. Intelligence Analysis (Orchestrator-Worker)**
-- **Overview**: Uses the Orchestrator-Worker pattern to coordinate parallel processing of OSINT feeds, threat intelligence, and geospatial data.
-- **Key Features**: Parallel processing, multi-source intelligence, hybrid RAG.
-- **Cookbook**: [:notebook: Intelligence Analysis Orchestrator-Worker](intelligence/02_Intelligence_Analysis_Orchestrator_Worker.ipynb)
-
----
-
-## **Renewable Energy**
-
-### **1. Energy Market Analysis**
-- **Overview**: Analyzes pricing trends and market movements using temporal market knowledge graphs.
-- **Key Features**: Market trend prediction, energy entity extraction.
-- **Cookbook**: [:notebook: Energy Market Analysis](renewable_energy/01_Energy_Market_Analysis.ipynb)
-
-### **2. Smart Grid Management**
-- **Overview**: Streams grid sensor data to monitor grid health and predict failures using temporal pattern detection.
-- **Key Features**: Real-time monitoring, failure prediction, anomaly detection.
-- **Cookbook**: [:notebook: Smart Grid Management](renewable_energy/02_Smart_Grid_Management.ipynb)
-
----
-
-## **Supply Chain**
-
-### **1. Supply Chain Data Integration**
-- **Overview**: Ingests logistics and supplier data to build a comprehensive supply chain knowledge graph.
-- **Key Features**: Logistics tracking, supplier relationship mapping.
-- **Cookbook**: [:notebook: Supply Chain Data Integration](supply_chain/01_Supply_Chain_Data_Integration.ipynb)
-
-### **2. Supply Chain Risk Management**
-- **Overview**: Detects risks in the supply chain by analyzing dependencies and external feeds.
-- **Key Features**: Risk detection, dependency analysis.
-- **Cookbook**: [:notebook: Supply Chain Risk Management](supply_chain/02_Supply_Chain_Risk_Management.ipynb)
-
----
-
-## **Trading**
-
-### **1. Risk Assessment**
-- **Overview**: Assesses portfolio risk using graph-based analytics, market simulations, and dependency mapping. Builds financial knowledge graphs to model portfolio relationships and perform risk analysis.
-- **Key Features**: Graph-based portfolio risk analysis, market simulation, dependency mapping, risk pattern detection.
-- **Pipeline**: `Portfolio Data → Build Financial KG → Graph Analytics → Risk Modeling → Dependency Analysis → Risk Assessment`.
-- **Cookbook**: [:notebook: Risk Assessment](trading/01_Risk_Assessment.ipynb)
-
-### **2. News Sentiment Analysis**
-- **Overview**: Extracts sentiment from financial news articles and correlates it with price movements using a comprehensive financial knowledge graph. Demonstrates semantic extraction, correlation analysis, and predictive modeling.
-- **Key Features**: Sentiment extraction, price correlation analysis, financial KG construction, predictive modeling.
-- **Pipeline**: `News Sources → Parse Articles → Extract Sentiment → Build Financial KG → Correlate with Prices → Predict Movements`.
-- **Cookbook**: [:notebook: News Sentiment Analysis](trading/02_News_Sentiment_Analysis.ipynb)
-