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}
}
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
python train.py
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}
}