https://github.com/baharefatemi/SimplE

Implementation of SimplE Embedding for Link Prediction in Knowledge Graphs in PyTorch
https://github.com/baharefatemi/SimplE

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Implementation of SimplE Embedding for Link Prediction in Knowledge Graphs in PyTorch

• Host: GitHub
• URL: https://github.com/baharefatemi/SimplE
• Owner: baharefatemi
• Created: 2019-02-22T20:07:18.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-08-23T20:01:59.000Z (about 4 years ago)
• Last Synced: 2024-08-02T13:22:27.918Z (3 months ago)
• Language: Python
• Size: 8.44 MB
• Stars: 115
• Watchers: 5
• Forks: 21
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
Summary

=======

This is a faster implementation of the model proposed in [SimplE Embedding for Link Prediction in Knowledge Graphs](https://papers.nips.cc/paper/7682-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:

    $ /models/D/

As an example, running the `SimplE` model on `wn18` will save the embeddings in the following folder:

    $ /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](https://mehran-k.github.io/) and [David Poole](http://www.cs.ubc.ca/~poole)

  [SimplE Embedding for Link Prediction in Knowledge Graphs](https://papers.nips.cc/paper/7682-simple-embedding-for-link-prediction-in-knowledge-graphs)

  

  [Representing and learning relations and properties under uncertainty](https://open.library.ubc.ca/collections/ubctheses/24/items/1.0375812)

## 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)  

License

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Licensed under the GNU General Public License Version 3.0.

Copyright (C) 2019 Bahare Fatemi