The datasets we used for experiments have been partially uploaded.
To obtain all the KGs, you can find it in https://drive.google.com/file/d/1oD1Gv2RbpNzO8GWGq7SusbAmYih5r-6Q/view?usp=sharing.
Make sure to put KGs from the Google Drive into OpenKE/benchmarks.
Update: The aligned files are updated and already put in the trainse_data/aligned folder.
- numpy
- tensorflow 1.xx
- tensorflow_probability
You need to run the baseline experiments to obtain the KG embeddings through the following code:
python Config.py baseline 300 100 1.0 -1
The parameters denotes mode, epoches, dimension, gan_ratio and pred_id respectively.
Note that if you want to try other embedding algorithms or some files like 1-1.txt is missing, you need to run n_n.py from OpenKE/benchmarks for each KG in /OpenKE/benchmarks/KG_1.
You can replace baseline with strategy_1 or strategy_2 to conduct the experiments with respect to FKGE.
By running baseline embeddings, you will create a experiment/ folder and the embeddings are inside experiment/0/ if you sepcify pred_id=-1.
After obtaining KG's initital embeddings from running the baseline model (make sure there are embeddings in the experiment/0/ folder), run:
python Config.py strategy_1 300 100 1.0 0
If you want to train FKGE with the PATE mechanism, in Config.py, replace
from FederalTransferLearning.hetro_AGCN_mul_dataset import GAN
with
from FederalTransferLearning.hetro_AGCN_mul_dataset_pate import GAN
If you use this code in your work, please kindly cite it.
@inproceedings{Peng-2021-DPFKGE,
title={Differentially Private Federated Knowledge Graphs Embedding},
author={Hao Peng and
Haoran Li and
Yangqiu Song and
Vincent W. Zheng and
Jianxin Li},
booktitle={CIKM 2021},
year={2021},
url={https://arxiv.org/abs/2105.07615}
}
Please send any questions about the code and/or the algorithm to hlibt@connect.ust.hk