HypER: Hypernetwork Knowledge Graph Embeddings

This codebase contains PyTorch implementation of the paper:

Hypernetwork Knowledge Graph Embeddings. Ivana Balažević, Carl Allen, and Timothy M. Hospedales. International Conference on Artificial Neural Networks, 2019. [Paper]

Running a model

To run the model, execute the following command:

CUDA_VISIBLE_DEVICES=0 python hyper.py --algorithm HypER --dataset FB15k-237

Available algorithms are:

HypER
HypE
DistMult
ComplEx
ConvE

Available datasets are:

FB15k-237
WN18RR
FB15k
WN18

To reproduce the results from the paper, use the following combinations of hyperparameters with batch_size=128, ent_vec_dim=200 and rel_vec_dim=200:

dataset	lr	dr	input_dropout	feature_map_dropout	hidden_dropout	label_smoothing
FB15k	0.005	0.995	0.2	0.2	0.3	0.
WN18	0.001	1.0	0.2	0.2	0.3	0.1
FB15k-237	0.0001	0.995	0.3	0.2	0.3	0.1
WN18RR	0.005	1.0	0.2	0.2	0.3	0.1

Requirements

The codebase is implemented in Python 3.6.6. Required packages are:

numpy      1.14.5
pytorch    0.4.0

Citation

If you found this codebase useful, please cite:

@inproceedings{balazevic2019hypernetwork,
title={Hypernetwork Knowledge Graph Embeddings},
author={Bala\v{z}evi\'c, Ivana and Allen, Carl and Hospedales, Timothy M},
booktitle={International Conference on Artificial Neural Networks},
year={2019}
}