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
- If you need to test your own dataset, please put it in
./datasetdirectory and format it according to our template. - 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