https://github.com/Mehran-k/SimplE

SimplE Embedding for Link Prediction in Knowledge Graphs
https://github.com/Mehran-k/SimplE

knowledge-base knowledge-base-completion knowledge-graph knowledge-graph-completion knowledge-graph-embeddings knowledgebase link-prediction relational-learning starai statistical-relational-learning tensor-factorization tensorflow

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SimplE Embedding for Link Prediction in Knowledge Graphs

• Host: GitHub
• URL: https://github.com/Mehran-k/SimplE
• Owner: Mehran-k
• License: other
• Created: 2018-02-13T18:38:19.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-02-11T16:15:36.000Z (almost 5 years ago)
• Last Synced: 2024-08-02T13:22:29.482Z (3 months ago)
• Topics: knowledge-base, knowledge-base-completion, knowledge-graph, knowledge-graph-completion, knowledge-graph-embeddings, knowledgebase, link-prediction, relational-learning, starai, statistical-relational-learning, tensor-factorization, tensorflow
• Language: Python
• Size: 8.42 MB
• Stars: 143
• Watchers: 8
• Forks: 34
• Open Issues: 1
• Metadata Files:
  • Readme: readme.md
  • License: LICENSE

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README

        
NEW

===

A much faster version (in PyTorch) is available here: https://github.com/baharefatemi/SimplE

Summary

=======

This software can be used to reproduce the results in our "SimplE Embedding for Link Prediction in Knowledge Graphs" paper. It can be also used to learn `SimplE` models for other datasets. The software can be also used as a framework to implement new tensor factorization models (implementations for `TransE` and `ComplEx` are included as two examples).

## Dependencies

* `Python` version 2.7

* `Numpy` version 1.13.1

* `Tensorflow` version 1.1.0

## Usage

To run a model `M` on a dataset `D`, do the following steps:

* `cd` to the directory where `main.py` is

* Run `python main.py -m M -d D`

Examples (commands start after $):

    $ python main.py -m SimplE_ignr -d wn18

    $ python main.py -m SimplE_avg -d fb15k

    $ python main.py -m ComplEx -d wn18

Running a model `M` on a dataset `D` will save the embeddings in a folder with the following address:

    $ /M_weights/D/

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

    $ /SimplE_ignr_weights/wn18/

## Learned Embeddings for SimplE

The best embeddings learned for `SimplE_ignr` and `SimplE_avg` on `wn18` and `fb15k` can be downloaded from [this link](https://drive.google.com/file/d/1fSxdFbSIcS4w4mAHUhKewjmXCcbOGqM7/view?usp=sharing) and [this link](https://drive.google.com/file/d/1hpDS34BxNfbr6xGeut5q5nvx8fW98qCe/view?usp=sharing) respectively.

To use these embeddings, place them in the same folder as `main.py`, load the embeddings and use them.

## 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

=======

Seyed Mehran Kazemi

Computer Science Department

The University of British Columbia

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

  

License

=======

Licensed under the GNU General Public License Version 3.0.

Copyright (C) 2018  Seyed Mehran Kazemi