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Incorporating Global Information in Local Attention for Knowledge Representation Learning

Source code for our paper: Incorporating Global Information in Local Attention for Knowledge Representation Learning

Requirements

  • conda
  • pytorch (version >= 1.1.0)

Dataset

The four public benchmark datasets for link prediction experiments with their folder names are given below.

  • Freebase: FB15k-237
  • Nell: NELL-995
  • Kinship: kinship
  • UMLS: umls

Training

When running for first time:

    $ sh prepare.sh

Then reproducing the results in the paper by:

  • Nell

      $ python3 main.py --get_2hop True --use_2hop True
    
  • Kinship

      $ python3 main.py --data ./data/kinship/ --output_folder ./checkpoints/kinship/out/ --lr 1e-2 --epochs_gat 4000 --epochs_conv 400 --batch_size_gat 8544 --drop_GAT 0.3 --weight_decay_conv 1e-5 --valid_invalid_ratio_conv 10 --out_channels 50 --drop_conv 0.0 --get_2hop True --use_2hop True
    
  • Other datasets

Parameters of other datasets are given in appendix of the paper.

For any comments or suggestions, please contact zhhan@connect.hku.hk

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