This code is an implementation of the paper, "Learning Representations of Bi-Level Knowledge Graphs for Reasoning beyond Link Prediction (AAAI 2023)".

This code is based on the OpenKE implementation, which is an open toolkit for knowledge graph embedding. Additional codes are written by Chanyoung Chung ([email protected]).

When you use this code or data, please cite our paper.

Chanyoung Chung and Joyce Jiyoung Whang, Learning Representations of Bi-Level Knowledge Graphs for Reasoning beyond Link Prediction, AAAI Conference on Artificial Intelligence (AAAI), 2023.

Use augment.py to perform data augmentation.

python augment.py [data] [conf]

• [data]: name of the dataset. The name should be the directory name of the dataset contained in the ./benchmarks folder.
• [conf]: threshold of the confidence score, i.e., $\tau$ in the paper.

To train BiVE-Q, use bive_q.py.

CUDA_VISIBLE_DEVICES=0 python bive_q.py [data] [learning_rate] [regul_rate] [epoch] --meta [weight_high] --aug [weight_aug] --lp/tp/clp

To train BiVE-B, use bive_b.py.

CUDA_VISIBLE_DEVICES=0 python bive_b.py [data] [learning_rate] [regul_rate] [epoch] --meta [weight_high] --aug [weight_aug] --lp/tp/clp