This is a faster implementation of the model proposed in SimplE Embedding for Link Prediction in Knowledge Graphs for knowledge graph embedding. It can be also used to learn SimplE
models for any input model. The software can be also used as a framework to implement new knowledge graph embedding models.
Python
version 3.6Numpy
version 1.15.4PyTorch
version 1.0.0To run SimplE you should define the following parameters:
ne
: number of epochs
lr
: learning rate
reg
:l2 regularization parameter
dataset
: The dataset you want to run SimplE on
emb_dim
: embedding dimension
neg_ratio
: number of negative examples per positive example
batch_size
: batch size
save_each
: validate every k epochs
python main.py -ne ne -lr lr -reg reg -dataset dataset -emb_dim emb_dim -neg_ratio neg_ratio -batch_size batch_size -save_each save_each
Running a model M
on a dataset D
will save the embeddings in a folder with the following address:
$ <Current Directory>/models/D/
As an example, running the SimplE
model on wn18
will save the embeddings in the following folder:
$ <Current Directory>/models/wn18/
In order to reproduce the results presented in the paper, you should run the following commands:
RUN python main.py -ne 1000 -lr 0.1 -reg 0.03 -dataset WN18 -emb_dim 200 -neg_ratio 1 -batch_size 1415 -save_each 50
RUN python main.py -ne 1000 -lr 0.05 -reg 0.1 -dataset FB15K -emb_dim 200 -neg_ratio 10 -batch_size 4832 -save_each 50
Refer to the following publication for details of the models and experiments.
Seyed Mehran Kazemi and David Poole
SimplE Embedding for Link Prediction in Knowledge Graphs
Representing and learning relations and properties under uncertainty
If you use this package for published work, please cite one (or both) of the following:
@inproceedigs{kazemi2018simple,
title={SimplE Embedding for Link Prediction in Knowledge Graphs},
author={Kazemi, Seyed Mehran and Poole, David},
booktitle={Advances in Neural Information Processing Systems},
year={2018}
}
@phdthesis{Kazemi_2018,
series={Electronic Theses and Dissertations (ETDs) 2008+},
title={Representing and learning relations and properties under uncertainty},
url={https://open.library.ubc.ca/collections/ubctheses/24/items/1.0375812},
DOI={http://dx.doi.org/10.14288/1.0375812},
school={University of British Columbia},
author={Kazemi, Seyed Mehran},
year={2018},
collection={Electronic Theses and Dissertations (ETDs) 2008+}
}
Bahare Fatemi
Computer Science Department
The University of British Columbia
201-2366 Main Mall, Vancouver, BC, Canada (V6T 1Z4)
Licensed under the GNU General Public License Version 3.0. https://www.gnu.org/licenses/gpl-3.0.en.html
Copyright (C) 2019 Bahare Fatemi