tahamajs/Sysmem2_in_AI/ComputerAssignments/CA10_knowledge_graphs/demos - Storage Bucket

Demos and Examples

Overview

This directory contains demonstration projects showing real-world applications of knowledge graphs.

Available Demos

1. Comprehensive Knowledge Graph Project

File: comprehensive_kg_project.py

A complete end-to-end knowledge graph project demonstrating:

• Knowledge graph construction
• Entity and relation extraction
• Embedding model training
• Reasoning and inference
• Visualization

Run:

python demos/comprehensive_kg_project.py

2. Healthcare Knowledge Graph

File: healthcare_kg_project.py

Medical knowledge graph for:

• Disease-symptom relationships
• Drug interactions
• Treatment recommendations
• Patient data analysis

Features:

• Medical entity recognition
• Clinical reasoning
• Drug interaction detection
• Treatment path analysis

Run:

python demos/healthcare_kg_project.py

3. Financial Knowledge Graph

File: financial_kg_project.py

Financial domain knowledge graph:

• Company relationships
• Market analysis
• Investment recommendations
• Risk assessment

Features:

• Company entity extraction
• Financial relationship mapping
• Market trend analysis
• Portfolio optimization

Run:

python demos/financial_kg_project.py

4. Retail Knowledge Graph

File: retail_kg_project.py

E-commerce and retail knowledge graph:

• Product relationships
• Customer preferences
• Recommendation systems
• Inventory management

Features:

• Product categorization
• Customer behavior analysis
• Personalized recommendations
• Supply chain optimization

Run:

python demos/retail_kg_project.py

Creating Your Own Demo

Template Structure

#!/usr/bin/env python3
"""
Custom Knowledge Graph Demo
"""

import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(__file__)))

from src.core.knowledge_graph import KnowledgeGraph
from src.models.embeddings import TransE
from src.reasoning.langgraph_reasoning import KnowledgeGraphReasoner

def main():
    # 1. Create knowledge graph
    kg = KnowledgeGraph()

    # 2. Add your domain data
    kg.add_entity("entity1", "type1")
    kg.add_relation("entity1", "relates_to", "entity2")

    # 3. Train embeddings
    model = TransE(embedding_dim=50)
    # ... training code

    # 4. Perform reasoning
    reasoner = KnowledgeGraphReasoner(kg)
    # ... reasoning code

    # 5. Visualize results
    kg.visualize()

    print("Demo completed successfully!")

if __name__ == "__main__":
    main()

Demo Requirements

Each demo should include:

1. Clear documentation: Explain what the demo does
2. Sample data: Include or generate sample data
3. Step-by-step execution: Clear execution flow
4. Visualization: Show results visually
5. Error handling: Handle common errors gracefully

Running All Demos

# Run all demos sequentially
for demo in demos/*.py; do
    echo "Running $demo..."
    python "$demo"
done

Demo Output

Demos save their output to:

• Visualizations: data/visualizations/demo_*.png
• Results: data/results/demo_*.json
• Logs: data/logs/demo_*.log

Contributing Demos

To contribute a new demo:

1. Create a new Python file in demos/
2. Follow the template structure
3. Add documentation
4. Test thoroughly
5. Update this README
6. Submit a pull request

Use Cases

Academic Research

• Citation networks
• Research collaboration graphs
• Topic modeling and trends

Business Intelligence

• Customer relationship management
• Market analysis
• Competitive intelligence

Healthcare

• Medical diagnosis support
• Drug discovery
• Patient care optimization

Social Networks

• Influence analysis
• Community detection
• Recommendation systems

Best Practices

1. Keep demos simple: Focus on one main concept
2. Use realistic data: Provide meaningful examples
3. Add comments: Explain complex operations
4. Handle errors: Gracefully handle edge cases
5. Clean up: Remove temporary files after execution

Support

For demo-related questions:

• Check the main documentation
• Review existing demos for examples
• Ask in community discussions

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Happy building! 🚀