/Demix

[Paper] Code for the ISWC 2023 paper "Negative Sampling with Adaptive Denoising Mixup for Knowledge Graph Embedding"

Primary LanguagePython

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}
}