FastKGE

The codes and datasets for "Fast and Continual Knowledge Graph Embedding via Incremental LoRA". [IJCAI 2024]

Framework

Folder Structure

The structure of the folder is shown below:

 FastKGE
 ├─checkpoint
 ├─data
 ├─logs
 ├─save
 ├─src
 ├─requirements.txt
 ├─data_to_id.py
 ├─cal_features.py
 ├─nodes_sort.py
 ├─main.py
 └README.md

Introduction to the structure of the folder:

• /checkpoint: The generated models are stored in this folder.
• /data: The datasets are stored in this folder.
• /logs: Logs for the training are stored in this folder.
• /save: Optional Results generated by models.
• /src: Source codes are of the method.
• requirements.txt: Required libraries.
• data_to_id.py, cal_features.py, and nodes_sort.py: To prepare the data processing.
• main.py: To run the FastKGE.
• README.md: Instruct on how to realize FastKGE.

Requirements

All experiments are implemented on the NVIDIA RTX 3090Ti GPU with the PyTorch. The version of Python is 3.7.

Please run as follows to install all the dependencies:

pip3 install -r requirements.txt

Usage

Preparation

1. Prepare the data processing in the shell:

python data_to_id.py
python cal_features.py
python nodes_sort.py

Run the code

2. Run the code with this in the shell:

./main.sh

Citation

If you find this method or code useful, please cite

@inproceedings{liu2024fast,
  title={Fast and Continual Knowledge Graph Embedding via Incremental LoRA},
  author={Jiajun Liu, Wenjun Ke, Peng Wang, Jiahao Wang, Jinhua Gao, Ziyu Shang, Guozheng Li, Zijie Xu, Ke Ji and Yining Li},
  booktitle={IJCAI},
  year={2024}
}