NSCaching

The Code for our paper "NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding" and the conference version has been published in ICDE2019 and long version has been publised in VLDBJ-2020.

The extension of NSCaching: "Efficient, Simple and Automated Negative Sampling for Knowledge Graph Embedding" has been accepte by the VLDB Journal.

Readers are welcomed to fork this repository to reproduce the experiments and follow our work. Please kindly cite our paper

@inproceedings{zhang2019nscaching,
  title={NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding},
  author={Zhang, Yongqi and Yao, Quanming and Shao, Yingxia and Chen, Lei},
  booktitle={International Conference on Data Engineering},
  pages={614--625},
  year={2019},
  organization={IEEE}
}

And the automated version

@article{zhang2020efficient,
    title{Efficient, Simple and Automated Negative Sampling for Knowledge Graph Embedding},
    author={Zhang, Yongqi and Yao, Quanming and Chen, Lei},
    booktitle={The VLDB journal},
    year={2020},
    publisher={Springer}
}

Instructions

For the sake of ease, a quick instruction is given for readers to reproduce the whole process on fb15k dataset. Note that the programs are tested on Linux(Ubuntu release 16.04), Python 3.7 from Anaconda 4.5.11.

Install PyTorch (>0.4.0)

conda install pytorch -c pytorch

Get this repo

git clone https://github.com/yzhangee/NSCaching
cd NSCaching

Get dataset from THUNLP-OpenKE

git clone https://github.com/thunlp/OpenKE
mv OpenKE/benchmarks ../KG_Data

NSCaching+scratch on FB15K

python train.py

NSCaching (auto) on FB15K

python auto_search.py   or    bash run_fb15k.sh

New Opportunities

• Interns, research assistants, and researcher positions are available. See requirement