GateTD: Gated Tensor Decomposition for Knowledge Graph Completion

This codebase contains PyTorch implementation of the paper:

GateTD: Gated Tensor Decomposition for Knowledge Graph Completion.

Link Prediction Results

Dataset	MRR	Hits@1	Hits@3	Hits@10
WN18RR	0.472	43.4	48.2	55.0
FB15k-237	0.347	25.3	38.0	53.8
WN18	0.951	94.6	95.5	96.0
FB15k	0.854	82.7	86.9	90.4

Running a model

To run the model, execute the following command:

 python learn.py --dataset FB15k-237 --max_epochs 1000 --batch_size 1024 --learning_rate 0.2
                                     --edim 2500 --rdim 1200 --reg 0.4 --init 0.001

Available datasets are:

WN18RR
FB15k-237
WN18
FB15k

To reproduce the results from the paper, use the following combinations of hyperparameters:

dataset	batch_size	learning_rate	edim	rdim	reg	init
WN18RR	1024	0.3	1000	500	0.5	0.001
FB15k-237	1024	0.2	2500	1200	0.4	0.001
WN18	1024	0.25	1000	1000	0.3	0.001
FB15k	1024	0.05	2500	1200	0.03	0.001

Requirements

The codebase is implemented in Python 3.6.5. Required packages are:

numpy      1.15.1
pytorch    1.0.1