https://github.com/ibalazevic/TuckER

TuckER: Tensor Factorization for Knowledge Graph Completion
https://github.com/ibalazevic/TuckER

Last synced: about 2 months ago
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TuckER: Tensor Factorization for Knowledge Graph Completion

• Host: GitHub
• URL: https://github.com/ibalazevic/TuckER
• Owner: ibalazevic
• License: mit
• Created: 2019-01-26T14:18:02.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-11-16T15:14:14.000Z (7 months ago)
• Last Synced: 2024-01-26T20:35:36.774Z (4 months ago)
• Language: Python
• Homepage:
• Size: 13.1 MB
• Stars: 332
• Watchers: 11
• Forks: 61
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• Awesome-pytorch-list - TuckER
• awesome-network-embedding - [Python PyTorch
• Awesome-pytorch-list-CNVersion - TuckER

README

        

## TuckER: Tensor Factorization for Knowledge Graph Completion



  



This codebase contains PyTorch implementation of the paper:

> TuckER: Tensor Factorization for Knowledge Graph Completion.

> Ivana Balažević, Carl Allen, and Timothy M. Hospedales.

> Empirical Methods in Natural Language Processing (EMNLP), 2019.

> [[Paper]](https://arxiv.org/pdf/1901.09590.pdf)

> TuckER: Tensor Factorization for Knowledge Graph Completion.

> Ivana Balažević, Carl Allen, and Timothy M. Hospedales.

> ICML Adaptive & Multitask Learning Workshop, 2019.

> [[Short Paper]](https://openreview.net/pdf?id=BkgREcHjnE)

### Link Prediction Results

Dataset | MRR | Hits@10 | Hits@3 | Hits@1

:--- | :---: | :---: | :---: | :---:

FB15k | 0.795 | 0.892 | 0.833 | 0.741

WN18 | 0.953 | 0.958 | 0.955 | 0.949

FB15k-237 | 0.358 | 0.544 | 0.394 | 0.266

WN18RR | 0.470 | 0.526 | 0.482 | 0.443

### Running a model

To run the model, execute the following command:

     CUDA_VISIBLE_DEVICES=0 python main.py --dataset FB15k-237 --num_iterations 500 --batch_size 128

                                           --lr 0.0005 --dr 1.0 --edim 200 --rdim 200 --input_dropout 0.3 

                                           --hidden_dropout1 0.4 --hidden_dropout2 0.5 --label_smoothing 0.1

Available datasets are:

    

    FB15k-237

    WN18RR

    FB15k

    WN18

    

To reproduce the results from the paper, use the following combinations of hyperparameters with `batch_size=128`:

dataset | lr | dr | edim | rdim | input_d | hidden_d1 | hidden_d2 | label_smoothing 

:--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---:

FB15k | 0.003 | 0.99 | 200 | 200 | 0.2 | 0.2 | 0.3 | 0.

WN18 | 0.005 | 0.995 | 200 | 30 | 0.2 | 0.1 | 0.2 | 0.1

FB15k-237 | 0.0005 | 1.0 | 200 | 200 | 0.3 | 0.4 | 0.5 | 0.1

WN18RR | 0.003 | 1.0 | 200 | 30 | 0.2 | 0.2 | 0.3| 0.1

    

### Requirements

The codebase is implemented in Python 3.6.6. Required packages are:

    numpy      1.15.1

    pytorch    1.0.1

    

### Citation

If you found this codebase useful, please cite:

    @inproceedings{balazevic2019tucker,

    title={TuckER: Tensor Factorization for Knowledge Graph Completion},

    author={Bala\v{z}evi\'c, Ivana and Allen, Carl and Hospedales, Timothy M},

    booktitle={Empirical Methods in Natural Language Processing},

    year={2019}

    }