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tucker's Introduction

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]

TuckER: Tensor Factorization for Knowledge Graph Completion. Ivana Balažević, Carl Allen, and Timothy M. Hospedales. ICML Adaptive & Multitask Learning Workshop, 2019. [Short 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	0.003	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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