Official code repository 📑 for NAACL 2024 Findings paper "Deja vu: Contrastive Historical Modeling with Prefix-tuning for Temporal Knowledge Graph Reasoning".
- python>=3.7
- torch>=1.6
- transformers>=4.15
All experiments are run with GeForce RTX 3090 GPUs.
We provide codes and instructions for running transductive TKG reasoning experiments.
Total 3 steps to follow: dataset preprocessing, model training, and model evaluation.
Datasets used in this repository are list in the directory $REPO_DIR/data. Besides, we've preprocessed the datasets, so you can skip step 1 (unless you want to preprocess your own datasets), directly run the training and evaluation scripts.
Taking ICEWS14 dataset as an example:
Step 1, preprocess the dataset
bash scripts/preprocess.sh ICEWS14
Step 2, training the model
CUDA_VISIBLE_DEVICES=0,1,2 OUTPUT_DIR=./checkpoint/ICEWS14/ bash scripts/train_icews14.sh
Step 3, evaluate the model
CUDA_VISIBLE_DEVICES=0 bash scripts/eval.sh ./checkpoint/ICEWS14/model_last.mdl ICEWS14
The trained model checkpoints and output files are saved in directory $REPO_DIR/checkpoint.
Note: For custom dataset, replace ICEWS14 to any dataset name that you want to run.
If you use our code in your research, please cite our work:
@inproceedings{peng2024deja,
title={Deja vu: Contrastive Historical Modeling with Prefix-tuning for Temporal Knowledge Graph Reasoning},
author={Peng, Miao and Liu, Ben and Xu, Wenjie and Jiang, Zihao and Zhu, Jiahui and Peng, Min},
booktitle={Findings of the Association for Computational Linguistics: NAACL 2024},
pages={1178--1191},
year={2024}
}