CKBC_Model

Dataset

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.

Training

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

Reproducing results

To reproduce the results published in the paper:

    $ python code/SIM_BERT_RGAT_ConvKB.py