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Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning

This is the official code release of the following paper:

Zixuan Li, Xiaolong Jin, Wei Li, Saiping Guan, Jiafeng Guo, Huawei Shen, Yuanzhuo Wang and Xueqi Cheng. Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning. SIGIR 2021 Full Paper.

regcn_architecture

Quick Start

Environment variables & dependencies

conda create -n regcn python=3.7

conda activate regcn

pip install -r requirement.txt

Process data

First, unzip and unpack the data files

tar -zxvf data-release.tar.gz

For the three ICEWS datasets ICEWS18, ICEWS14, ICEWS05-15, go into the dataset folder in the ./data directory and run the following command to construct the static graph.

cd ./data/<dataset>
python ent2word.py

Train models

Then the following commands can be used to train the proposed models. By default, dev set evaluation results will be printed when training terminates.

  1. Train models
cd src
python main.py -d ICEWS14s --train-history-len 3 --test-history-len 3 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --gpu=0 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --weight 0.5  --entity-prediction --relation-prediction --add-static-graph --angle 10 --discount 1 --task-weight 0.7 --gpu 0

Evaluate models

To generate the evaluation results of a pre-trained model, simply add the --test flag in the commands above.

For example, the following command performs single-step inference and prints the evaluation results (with ground truth history).

python main.py -d ICEWS14s --train-history-len 3 --test-history-len 3 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --gpu=0 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --weight 0.5  --entity-prediction --relation-prediction --add-static-graph --angle 10 --discount 1 --task-weight 0.7 --gpu 0 --test

The following command performs multi-step inference and prints the evaluation results (without ground truth history).

python main.py -d ICEWS14s --train-history-len 3 --test-history-len 3 --dilate-len 1 --lr 0.001 --n-layers 2 --evaluate-every 1 --gpu=0 --n-hidden 200 --self-loop --decoder convtranse --encoder uvrgcn --layer-norm --weight 0.5  --entity-prediction --relation-prediction --add-static-graph --angle 10 --discount 1 --task-weight 0.7 --gpu 0 --test --multi-step --topk 0

Change the hyperparameters

To get the optimal result reported in the paper, change the hyperparameters and other experiment set up according to Section 5.1.4 in the paper (https://arxiv.org/abs/2104.10353).

Citation

If you find the resource in this repository helpful, please cite

@article{li2021temporal,
  title={Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning},
  author={Li, Zixuan and Jin, Xiaolong and Li, Wei and Guan, Saiping and Guo, Jiafeng and Shen, Huawei and Wang, Yuanzhuo and Cheng, Xueqi},
  booktitle={SIGIR},
  year={2021}
}