Cross-Model Entity Alignment 🚀

🔍 Project Overview

Cross-Model Entity Alignment is an AI-driven project focused on aligning entities between RDF graphs and Property Graphs (PGs). Using transformer-based encoders and contrastive learning, we aim to generate highly accurate vector representations of graph entities and establish meaningful mappings between them.

🛠️ Key Features

• Dual Graph Representation: Supports RDF triples and Property Graph structures.
• Transformer-Based Encoding: Converts graphs into vector spaces for comparison.
• Contrastive Learning: Enhances entity alignment via similarity optimization.
• Graph-to-Graph Mapping: Creates a structured mapping between RDF and PG entities.

📌 How It Works

1. Pretrain Encoders: Train individual models for RDF and PG representations.
2. Fine-Tune with Contrastive Loss: Optimize similarity scores for aligned entities.
3. Entity Matching: Identify equivalent nodes across both graph formats.
4. Evaluate Alignment: Measure accuracy with standard metrics.

📂 Repository Structure

/cross-model-entity-alignment/
├── data/
│   ├── rdf/
│   │   └── toy_dbpedia.ttl
│   ├── pg/
│   │   ├── nodes.csv
│   │   └── edges.csv
│   └── alignments.csv
├── encoders/
│   ├── rdf_encoder.py
│   └── pg_encoder.py
└── README.md

🤝 Contributing

Want to help align some graphs? Feel free to fork, submit PRs, or raise issues!

📜 License

MIT License - Use freely, but don't forget to give credit! 😉

---

⚡ Cross-Model Entity Alignment: Where RDF Meets PG! 🌉