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Use Cases

Semantica is designed to solve complex data challenges across various domains. This guide explores common use cases and how to implement them.

About This Guide

This guide provides detailed implementation guides for real-world use cases, complete with code examples, prerequisites, and step-by-step instructions.

Use Case Comparison

Use Case Difficulty Time Domain Key Features Cookbook
Biomedical Knowledge Graphs Intermediate 1-2 hours Healthcare Gene-protein-disease relationships Drug Discovery, Genomic Variant Analysis
Financial Data Integration Intermediate 1-2 hours Finance MCP integration, real-time data Financial Data Integration MCP
Fraud Detection Advanced 2-3 hours Finance Temporal graphs, pattern detection Fraud Detection
Blockchain Analytics Intermediate 1-2 hours Finance Transaction tracing, DeFi intelligence DeFi Protocol Intelligence, Transaction Network
Cybersecurity Threat Intelligence Advanced 2-3 hours Security Threat mapping, anomaly detection Real-Time Anomaly Detection, Threat Intelligence
Intelligence Analysis Intermediate 1-2 hours Security Criminal networks, OSINT analysis Criminal Network Analysis, Intelligence Orchestrator
Supply Chain Optimization Intermediate 1-2 hours Industry Data integration, route optimization Supply Chain Data Integration
Renewable Energy Management Intermediate 1-2 hours Energy Energy market analysis, optimization Energy Market Analysis
GraphRAG Advanced 1-2 hours AI Enhanced RAG with knowledge graphs GraphRAG Complete, RAG vs GraphRAG

Difficulty Levels: - Beginner: Basic Semantica knowledge required - Intermediate: Some domain knowledge helpful - Advanced: Requires domain expertise and advanced Semantica features

Research & Science

  • Biomedical Knowledge Graphs --- Accelerate drug discovery and understand disease pathways by connecting genes, proteins, drugs, and diseases.

    Goal: Connect genes, proteins, drugs, and diseases from scientific literature and databases.

    Difficulty: Intermediate

    Drug Discovery Pipeline

    Genomic Variant Analysis

Biomedical Knowledge Graphs Implementation

Prerequisites: - Domain knowledge of biomedical concepts - Access to biomedical literature/databases

Implementation Guides:

  • Drug Discovery Pipeline Cookbook: Build knowledge graphs from PubMed RSS feeds
  • Topics: PubMed RSS ingestion, entity-aware chunking, GraphRAG, vector similarity search
  • Difficulty: Intermediate
  • Time: 1-2 hours

  • Use Cases: Drug discovery, biomedical research

  • Genomic Variant Analysis Cookbook: Analyze genomic variants using temporal knowledge graphs

  • Topics: bioRxiv RSS, temporal KGs, deduplication, pathway analysis
  • Difficulty: Intermediate
  • Time: 1-2 hours
  • Use Cases: Genomic research, variant analysis

Finance & Trading

  • Financial Data Integration --- Integrate financial data from multiple sources using MCP servers and real-time ingestion.

    Goal: Connect Alpha Vantage API, MCP servers, seed data, and real-time ingestion for comprehensive financial analysis.

    View Cookbook

  • Fraud Detection --- Detect complex fraud rings using temporal knowledge graphs and pattern detection.

    Goal: Build a graph of Users, Devices, IP Addresses, and Transactions to find cycles and detect fraud patterns.

    View Cookbook

  • Blockchain Analytics --- Analyze DeFi protocols and transaction networks for intelligence and fraud detection.

    Goal: Map transaction flows between wallets and exchanges, analyze DeFi protocols, and detect illicit activity.

    DeFi Protocol Intelligence

    Transaction Network Analysis

Security & Intelligence

  • Cybersecurity Threat Intelligence --- Proactively identify and mitigate cyber threats using real-time anomaly detection and threat intelligence.

    Goal: Ingest threat feeds (CVE databases, security RSS), detect anomalies in streaming data, and build threat intelligence knowledge graphs.

    Real-Time Anomaly Detection

    Threat Intelligence Hybrid RAG

  • Criminal Network Analysis --- Analyze criminal networks to identify key players, communities, and suspicious patterns using OSINT RSS feeds, deduplication, and network centrality analysis.

    Goal: Build knowledge graphs from police reports, court records, and surveillance data.

    View Cookbook

  • Intelligence Analysis Orchestrator Worker --- Comprehensive intelligence analysis using pipeline orchestrator with multiple RSS feeds, conflict detection, and multi-source integration.

    Goal: Process multiple intelligence sources in parallel using orchestrator-worker pattern.

    View Cookbook

Industry & Operations

  • Supply Chain Optimization --- Visualize and optimize complex global supply chains.

    Goal: Map suppliers, logistics routes, and inventory levels to identify bottlenecks.

    View Cookbook

  • Renewable Energy Management --- Optimize grid operations and asset maintenance.

    Goal: Connect sensor data, weather forecasts, and maintenance logs to predict failures.

    View Cookbook

Advanced AI Patterns

  • Graph-Augmented Generation (GraphRAG) --- Enhance LLM responses with structured ground truth using knowledge graphs.

    Goal: Use the knowledge graph to retrieve precise context for RAG applications with hybrid retrieval and logical inference.

    GraphRAG Complete

    RAG vs GraphRAG Comparison

Summary

This guide covered use cases across multiple domains with corresponding cookbooks:

  • Research & Science: Biomedical knowledge graphs (Drug Discovery, Genomic Variant Analysis)
  • Finance & Trading: Financial data integration, fraud detection, blockchain analytics
  • Security & Intelligence: Cybersecurity threat intelligence, criminal network analysis, intelligence orchestration
  • Industry: Supply chain optimization, renewable energy management
  • AI Applications: GraphRAG (Complete implementation and comparison)

Next Steps

Contribute

Have a use case to add? Contribute on GitHub