ComputerAssignments/CA10_knowledge_graphs/docs/README_RUN.md

CA10 Knowledge Graphs - Complete Project Execution

📋 Project Description

This project includes a complete implementation of Knowledge Graph systems with the following capabilities:

• Basic Knowledge Graph Creation: Building and visualizing simple knowledge graphs
• Advanced Scientific Knowledge Graph: Analyzing scientific networks with temporal and influence features
• Embedding Models: TransE, DistMult, and ComplEx
• Reasoning and Querying: Reasoning examples and subgraph extraction
• Advanced Visualization: Saving all images in the visualization folder

🚀 How to Run

Method 1: Complete Execution with Script

./run.sh

Method 2: Manual Execution

# Install dependencies (no virtual environment needed)
pip install -r requirements.txt

# Run main script
python main_execution.py

📁 File Structure

CA10_knowledge_graphs/
├── run.sh                    # Complete execution script
├── main_execution.py         # Main Python script
├── requirements.txt          # Project dependencies
├── CA10.ipynb               # Main notebook
├── src/                     # Source code
│   ├── core/               # Main knowledge graph classes
│   ├── models/             # Embedding models
│   ├── reasoning/          # Reasoning algorithms
│   └── utils/              # Utility tools
├── visualization/          # Generated images
├── results/               # Analysis results
└── logs/                  # Log files

🎯 Generated Outputs

Images (visualization/)

• basic_knowledge_graph.png - Basic knowledge graph visualization
• advanced_scientific_network.png - Advanced scientific network analysis
• transe_embeddings.png - TransE embeddings visualization
• distmult_embeddings.png - DistMult embeddings visualization
• complex_embeddings.png - ComplEx embeddings visualization
• einstein_subgraph.png - Einstein's subgraph

Results (results/)

• scientific_kg_analysis_*.json - Complete scientific graph analysis
• execution_report_*.md - Project execution report

🔧 Script Features

• Automatic Execution: All steps are executed automatically
• Image Saving: All images are saved in high quality (300 DPI)
• Reporting: Complete execution report is generated
• Error Management: Appropriate error messages are displayed
• System Environment: Uses system Python environment (no virtual environment)

📊 Sample Output

After successful execution, the following message is displayed:

🎉 CA10 Knowledge Graphs execution completed successfully!
==================================================
📁 Generated files:
   • visualization/ - All plots and figures
   • results/ - Analysis results and reports
   • logs/ - Execution logs

🛠️ Troubleshooting

Issue: Dependency Installation Error

pip install --upgrade pip
pip install -r requirements.txt --no-cache-dir

Issue: Execution Permission Error

chmod +x run.sh

Issue: Python Path Error

# Make sure Python is in PATH
which python3
python3 --version

🎯 Advanced Features

API Server

# Start API server
python start_api.py --kg-type scientific --port 8000

Advanced Execution

# Run with advanced features
python main_execution_advanced.py

📚 Documentation

• Main Documentation: README.md
• Advanced Features: README_ADVANCED.md
• API Documentation: Available at http://localhost:8000/docs when server is running

🔍 Testing

# Test setup
python test_setup.py

# Test API keys
python test_api_keys.py

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Ready to explore Knowledge Graphs! 🚀