Demix

Code and datasets for paper "Negative Sampling with Adaptive Denoising Mixup for Knowledge Graph Embedding" accepted by ISWC'23.

Setup

We check the reproducibility under this environment.

• Python 3.9.0
• CUDA 10.1
• pytorch-lightning 1.6.5

To run the codes, you need to install the requirements:

git clone https://github.com/DeMix2023/Demix.git
cd Demix

conda create -n demix python=3.9
conda activate demix
pip install -r requirements.txt

Train Demix

You can try our code easily by runing the scripts in ./script, for example:

bash ./script/run_transe_fb.sh

The training process, validation results, and final test results will be printed and saved in the corresponding log file. After training, you can find training logs in ./wandb. We put the trained model state dicts in ./output.

Acknowledgment

The repository benefits greatly from NeuralKG. Thanks a lot for their excellent work.

Citation

Please cite our paper if you use our model in your work:

@inproceedings{Demix,
  title     = {Negative Sampling with Adaptive Denoising Mixup for Knowledge Graph Embedding},
  author    = {Chen, Xiangnan and Zhang, Wen and Yao, Zhen and Chen, Mingyang and Tang, Siliang},
  booktitle = {{ISWC}},
  series    = {Lecture Notes in Computer Science},
  pages     = {253--270},
  publisher = {Springer},
  year      = {2023}
}