NSCaching

The Code for our paper "NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding" and this paper has been accepted by ICDE2019.

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={2019 IEEE 35th International Conference on Data Engineering (ICDE)},
  pages={614--625},
  year={2019},
  organization={IEEE}
}

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

Future Works

To easy the use of NSCaching, please find tools discussed in our AutoML survey paper:

@techreport{yao2018automl,
  title={Taking Human out of Learning Applications: A Survey on Automated Machine Learning},
  author={Yao, Quanming and Wang, Mengshuo},
  institution={arXiv preprint arXiv:1810.13306},
  year={2018}
}