KG2PG: Knowledge Graph to Property Graph Transformation

Transform RDF graph data models to Property Graph (PG) data models using the S3PG algorithm.

🚀 Quick Start

Test with included sample data:

git clone https://github.com/dkw-aau/KG2PG.git
cd KG2PG
docker build -t kg2pg:dockerImage .
cd scripts
./test_runningExample.sh

📖 For Reviewers

→ See REPRODUCIBILITY.md - Complete guide for reproducing paper results

This guide covers:

• Hardware requirements (what you can reproduce on 8GB/32GB/64GB RAM)
• Step-by-step instructions
• Validation commands
• Common issues and solutions
• How to write your reproducibility report

🔄 Reproducing Paper Results

Note: Paper used 256GB RAM. Different datasets have different requirements.

What You Can Reproduce

Your RAM	Datasets You Can Run
8GB+	Running Example
32GB+	Running Example + DBpedia 2020 + Bio2RDF ✅
64GB+	All datasets including DBpedia 2022

Quick Reproduction

1. Download data (datasets + SHACL shapes):

   • DBpedia-2020 | DBpedia-2022 | Bio2RDF
   • SHACL Shapes

2. Place in data/ directory

3. Run:

   docker build -t kg2pg:dockerImage .
   cd scripts
   ./run_dbp2020.sh      # 32GB RAM, ~10-15 min

4. Validate:

   ls -lh output/DBpedia2020/
   wc -l output/DBpedia2020/*/PG_NODES_WD_LABELS.csv

→ Full instructions: REPRODUCIBILITY.md

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📖 Documentation

For Reviewers:

• REPRODUCIBILITY.md ⭐ Complete reproduction guide

For Users:

• Docker Usage - Docker setup and usage
• JAR Usage - Standalone JAR instructions
• Output Format - Generated files explained
• Neo4j Import - Load data into Neo4j
• Build Instructions - Build from source

Troubleshooting:

• Validation Guide - Verify results
• Hardware Requirements - Memory requirements
• Docker Testing - Debug issues

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📦 JAR (Alternative)

# Download and run
wget https://github.com/dkw-aau/KG2PG/releases/download/v1.0.4/kg2pg-v1.0.4.jar
java -jar kg2pg-v1.0.4.jar

➡️ See JAR_USAGE.md for complete guide

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🛠️ Build from Source

# Build Docker image
./build-docker.sh

# Build JAR
./build-jar.sh

➡️ See BUILD_INSTRUCTIONS.md for complete guide

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📁 Output Files

Each run generates:

• PG_NODES_WD_LABELS.csv - Node labels
• PG_NODES_LITERALS.csv - Literal properties
• PG_RELATIONS.csv - Relationships/edges
• PG_PREFIX_MAP.csv - Namespace mappings
• PG_NODES_PROPS_JSON.json - Key-value properties
• PG_SCHEMA.txt - Schema definition
• *_RUNTIME_LOGS.csv - Performance metrics

➡️ See OUTPUT_FORMAT.md for detailed format specs

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📥 Neo4j Import

# Quick import
docker exec neo4j neo4j-admin database import full \
  --delimiter="|" --array-delimiter=";" \
  --nodes=import/runningExample/PG_NODES_LITERALS.csv \
  --nodes=import/runningExample/PG_NODES_WD_LABELS.csv \
  --relationships=import/runningExample/PG_RELATIONS.csv \
  neo4j

➡️ See NEO4J_IMPORT.md for complete setup

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🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: JAR_USAGE.md

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🏆 Citation

If you use KG2PG in your research, please cite our work:

@article{kg2pg2024,
  title={KG2PG: Knowledge Graph to Property Graph Transformation using S3PG},
  author={Your Authors},
  journal={Your Journal},
  year={2024}
}

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Made with ❤️ for the Knowledge Graph community