AAE_FOR_KG

Using Autoencoders for Knowledge Graph Embedding

Introduction

This is the Pytorch implementaion of some knowledge graph embedding models based on adversarial autoencoders. And we have test these models with drug-drug interaction datasets.

Dependencies

This project uses Python 3.5.3, with the following lib dependencies:

• Pytorch 1.3

• Numpy 1.15.1

• Sklearn 0.21

Usage

1. If you need to test your own dataset, please put it in ./dataset directory and format it according to our template.
2. Then you can run commend as follows to train/test/valid the models. For example, this command train a RotatE model on Deepddi dataset.

python3 -u main_rotate.py -ne 1000 -D_lr 0.5 -G_lr 0.001 -reg 0.3 -dataset Deepddi -emb_dim 200 -neg_ratio 1 -batch_size 512 -save_each 100 -discriminator_range 1