Awesome

Summary

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.

Dependencies

Python version 3.6
Numpy version 1.15.4
PyTorch version 1.0.0

Usage

To 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

Run 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/

Reproducing the Results in the Paper

In order to reproduce the results presented in the paper, you should run the following commands:

WN18

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

FB15K

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

Learned Embeddings for SimplE

Publication

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

Cite SimplE

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+}
}

Contact

Bahare Fatemi

Computer Science Department

The University of British Columbia

201-2366 Main Mall, Vancouver, BC, Canada (V6T 1Z4)

bfatemi@cs.ubc.ca

License

Licensed under the GNU General Public License Version 3.0. https://www.gnu.org/licenses/gpl-3.0.en.html