EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs

This repository provides the code and data of the paper "EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs", Namyong Park, Fuchen Liu, Purvanshi Mehta, Dana Cristofor, Christos Faloutsos, and Yuxiao Dong, The Fifteenth ACM International Conference on Web Search and Data Mining (WSDM) 2022.

Setup

Run script/setup_evokg.sh to create a conda environment named evokg and install required packages.

Datasets

Datasets used in our paper can be found in the data folder. No additional data preprocessing is needed to run the code.

Running EvoKG

Scripts in script/link_pred/ and script/time_pred/ can be used to run EvoKG for temporal link prediction and event time prediction, respectively. Execution logs and results are stored in the result folder by default. To save results in a different folder, update settings.py accordingly.

Citing

If you use the code or datasets in this repository, please cite our paper.

@inproceedings{park2022evokg,
    title={{EvoKG}: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs},
    author={Namyong Park and Fuchen Liu and Purvanshi Mehta and Dana Cristofor and Christos Faloutsos and Yuxiao Dong},
    booktitle={{WSDM}},
    year={2022},
}