The ConceptNet datasets are stored in the data folder, and the training, test, and validation sets are train.txt, test.txt, and dev.txt, respectively. the fine-tuned trained BERT model weights from the paper are stored in the link(password: bs45), and the folder in which the link is downloaded should be placed in the ConceptNet folder.
Parameters:
--epochs_gat: Number of epochs for gat training.
--epochs_conv: Number of epochs for convolution training.
--lr: Initial learning rate.
--weight_decay_gat: L2 reglarization for gat.
--weight_decay_conv: L2 reglarization for conv.
--get_2hop: Get a pickle object of 2 hop neighbors.
--use_2hop: Use 2 hop neighbors for training.
--partial_2hop: Use only 1 2-hop neighbor per node for training.
--output_folder: Path of output folder for saving models.
--batch_size_gat: Batch size for gat model.
--valid_invalid_ratio_gat: Ratio of valid to invalid triples for GAT training.
--drop_gat: Dropout probability for attention layer.
--alpha: LeakyRelu alphas for attention layer.
--nhead_GAT: Number of heads for multihead attention.
--margin: Margin used in hinge loss.
--batch_size_conv: Batch size for convolution model.
--alpha_conv: LeakyRelu alphas for conv layer.
--valid_invalid_ratio_conv: Ratio of valid to invalid triples for conv training.
--out_channels: Number of output channels in conv layer.
--drop_conv: Dropout probability for conv layer.
The specific value settings for all parameters are included in the code
To reproduce the results published in the paper:
$ python code/SIM_BERT_RGAT_ConvKB.py