NASE
Codes for our CIKM 2020 paper NASE: Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search.
Xiaoyu Kou, Bingfeng Luo, Huang Hu, Yan Zhang.
Prerequisites
- Linux
- Python 3.6
- PyTorch >= 1.2.0
- numpy: 1.18.5
Note: This code framework is based on Paper Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
Getting Started
Installation
Clone this repo.
git clone https://github.com/KXY-PUBLIC/NASE.git
cd NASEPlease install dependencies by
pip install -r requirements.txtDataset
- FB15K-237 and WN18RR are two well-known KG benchmark datasets.
- Medical-E, Medical-C and Military are three less common datasets to better compare the dataset adaptation ability of each system.
Searching Examples:
For example, searching on FB15k-237 datasets:
mkdir log/
CUDA_VISIBLE_DEVICES=0 nohup python -u search.py --dataset=FB15k-237 --epochs=800 --model_name=NASE --layers=1 --do_margin_loss=1 --embedding_size=200 &> log/search_NASE_fb.out &
Searching on Medical-E datasets:
CUDA_VISIBLE_DEVICES=0 nohup python -u search.py --dataset=Medical-E --epochs=800 --model_name=NASE --layers=2 --do_margin_loss=1 --embedding_size=100 &> log/search_NASE_Medical-E.out &
Training Examples:
For example, training on FB15k-237 datasets:
CUDA_VISIBLE_DEVICES=0 nohup python -u train.py --dataset=FB15k-237 --epochs=1000 --model_name=NASE --arch=arc_1 --lr=1e-3 --embedding_size=400 &> log/NASE_FB15k-237_arc_1.out &
Training on WN18RR datasets:
CUDA_VISIBLE_DEVICES=0 nohup python -u train.py --dataset=WN18RR --epochs=1000 --model_name=NASE --arch=arc_2 --lr=1e-2 --embedding_size=200 &> log/NASE_WN18RR_arc_2.out &
Training on Medical-E datasets:
CUDA_VISIBLE_DEVICES=0 nohup python -u train.py --dataset=Medical-E --epochs=1000 --model_name=NASE --arch=arc_3 --lr=1e-2 --embedding_size=200 &> log/NASE_Medical-E_arc_3.out &
Training on Medical-C datasets:
CUDA_VISIBLE_DEVICES=0 nohup python -u train.py --dataset=Medical-C --epochs=1000 --model_name=NASE --arch=arc_1 --lr=1e-2 --embedding_size=200 &> log/NASE_Medical-C_arc_1.out &
Training on Military datasets:
CUDA_VISIBLE_DEVICES=0 nohup python -u train.py --dataset=Military --epochs=1000 --model_name=NASE --arch=arc_2 --lr=1e-2 --embedding_size=200 &> log/NASE_Military_arc_2.out &
Cite
Please cite our paper if you use this code in your own work:
@inproceedings{kou2020nase,
title={NASE: Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search},
author={Kou, Xiaoyu and Luo, Bingfeng and Hu, Huang and Zhang, Yan},
booktitle={Proceedings of the 29th ACM International Conference on Information \& Knowledge Management},
pages={2089--2092},
year={2020}
}
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