Structure Aware Negative Sampling in Knowledge Graphs

Introduction

This repository provides the PyTorch implementation of Structure Aware Negative Sampling (SANS) technique presented in Structure Aware Negative Sampling in Knowledge Graphs paper as well as several popular KGE models.

Execution

As an example, the following command trains and validates a TransE model on wn18rr dataset by using RW-SANS with 1000 random walks on 3-Hop neighborhood:

CUDA_VISIBLE_DEVICES=0 python -u codes/run.py --cuda \
    --do_train --do_valid \
    --data_path data/wn18rr \
    --model TransE \
    -n 256 -b 1024 -d 1000 \
    -g 24.0 -a 1.0 \
    -lr 0.0001 --max_steps 150000 \
    -save models/TransE_wn18rr_0 --test_batch_size 16 \
    -khop 3 -nrw 1000

To check all the available arguments, you can run python codes/run.py --help.

Reproducibility

To reproduce the results presented in the EMNLP 2020 paper Structure Aware Negative Sampling in Knowledge Graphs, you can use the commands provided in best_config.sh.

Infrustrucutre

All experiments were carried on a server with one NVIDIA V100 GPU, 10 CPU cores, and 46GB RAM.

Acknowledgments

Our implemention is based on the PyTorch implementation of RotatE model provided here. This research was enabled in part by support provided by Calcul Québec and Compute Canada.