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Knowledge Graph Embedding and Graph-Based Models for Leak Testing System

This repository implements several Knowledge Graph Embedding (KGE) and Graph-Based models to analyze data from a leak testing system. The models are trained and evaluated on a dataset derived from the original Neo4j database of the leak testing system.

Repository Structure

  • Main.py: The main script to train and evaluate the models.
  • dataset/: Contains the training, validation, and test datasets.
    • train_enhanced.txt: Training dataset.
    • valid_enhanced.txt: Validation dataset.
    • test_enhanced.txt: Testing dataset.
  • results_1000.json: Results for Knowledge Graph Embedding models.
  • results_graph_1000.json: Results for Graph-Based models.

Prerequisites

  • Python >= 3.8
  • Required packages listed in requirements.txt

Installation

  1. Clone the repository: git clone https://github.com/yourusername/your-repo-name.git cd your-repo-name

  2. Install the required Python packages: pip install -r requirements.txt

Dataset Description

  • Original Dataset: Derived from the leak testing system and the corresponding Neo4j knowledge graph database.
  • Enhanced Dataset: Preprocessed and split into:
    • train_enhanced.txt (training)
    • valid_enhanced.txt (validation)
    • test_enhanced.txt (testing)

Running the Main Script

Main.py performs the following steps:

  1. Loads the dataset from the dataset/ folder.
  2. Trains several KGE and Graph-Based models.
  3. Evaluates the models on the test dataset.
  4. Saves the results to results_1000.json and results_graph_1000.json.

Run the script with: python Main.py

Results

  • results_1000.json: Contains evaluation metrics (e.g., Hits@K, MRR) for Knowledge Graph Embedding models.
  • results_graph_1000.json: Contains evaluation metrics for Graph-Based models.

Models Trained

Knowledge Graph Embedding Models:

  • TransE
  • RotatE
  • ComplEx
  • DistMult
  • TransH
  • TransR
  • TransD

Graph-Based Models:

  • GraphSAGE
  • R-GCN
  • A2N
  • CompGCN
  • SE-GNN
  • Convolutional GNN (if applicable)

Contributing

Feel free to open an issue or submit a pull request if you would like to contribute to the project.

License

This project is licensed under the MIT License.