TuckER: Tensor Factorization for Knowledge Graph Completion

This codebase contains PyTorch implementation of the paper:

TuckER: Tensor Factorization for Knowledge Graph Completion. Ivana Balaevi, Carl Allen, and Timothy M. Hospedales. arXiv preprint arXiv:1901.09590, 2019. [Paper]

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 | 0.01 | 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      1.14.5
pytorch    0.4.0

Citation

If you found this codebase useful, please cite:

@article{balazevic2019tucker,
title={TuckER: Tensor Factorization for Knowledge Graph Completion},
author={Balav{z}evi'c, Ivana and Allen, Carl and Hospedales, Timothy M},
journal={arXiv preprint arXiv:1901.09590},
year={2019}
}