Complex Embeddings for Simple Link Prediction

This repository contains the code for experiments in the paper:

Complex Embeddings for Simple Link Prediction, Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier and Guillaume Bouchard, ICML 2016.

Install

First clone the repository:

git clone https://github.com/ttrouill/complex.git

The code dependends on downhill, a theano based Stochastic Gradient Descent implementation.

Install it, along with other dependencies with:

pip install -r requirements.txt

Run the experiments

To run the experiments, unpack the datasets first:

unzip datasets/fb15k.zip -d datasets/
unzip datasets/wn18.zip -d datasets/

And run the corresponding python scripts, for Freebase (FB15K):

python fb15k_run.py

And for Wordnet (WN18):

python wn18_run.py

By default, it runs the ComplEx (Complex Embeddings) model, edit the files and uncomment the corresponding lines to run DistMult, TransE or CP models. The given hyper-parameters for each model are the best validated ones by the grid-search described in the paper.

To run on GPU (approx 5x faster), simply add the following theano flag before the python call:

THEANO_FLAGS='device=gpu' python fb15k_run.py

Citing ComplEx

If you use this package for published work, please cite the paper. Here is the BibTeX:

@inproceedings{trouillon2016complex,
	author = {Trouillon, Th\'eo and Welbl, Johannes and Riedel, Sebastian and Gaussier, \'Eric and Bouchard, Guillaume},
	booktitle = {International Conference on Machine Learning (ICML)},
	title = {{Complex embeddings for simple link prediction}},
	volume={48},
	pages={2071--2080},
	year = {2016}
}

License

This software comes under a non-commercial use license, please see the LICENSE file.