We use four entity alignment datasets EN-FR-15K, EN-DE-15K, D-W-15K, and D-Y-15K in our experiments, which can be downloaded from OpenEA. We also use ICNS in our experiments.
We recommend creating a new conda environment to install and run CAEA.
conda create -n KGA python==3.7
conda activate KGA
pip install -r requirements.txtCAEA use Bert to convert attribute-literals into vectors. You should download Bert from bert-base-uncased or Official website and save it to KGA/bert-base-uncased folder.
run python main.py. In order to improve the training efficiency, the program will first embed the literal representation of the entire KG, which will take a long time. Then start training and testing.
To facilitate direct testing of this code, we provide a dataset with embedded representations D-W-15K. After downloading predata/AVH.pkl and put it to d_w_15k/predata/AHV.pkl, you can directly test the operation of the model.
If you have any difficulty or question in running code and reproducing experimental results, please leave messages