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TuckER

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TuckER: Tensor Factorization for Knowledge Graph Completion

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## 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}
    }

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