GTRL

This is the source code for paper [GTRL: An Entity Group-Aware Temporal Knowledge Graph Representation Learning Method]

Data

We processed the ICEWS [1] and GDELT [2] and got three country based datasets:

GDELT18

ICEWS18

ICEWS14

Prerequisites

Python 3.7.7
PyTorch 1.6.0
dgl 0.5.0
Sklearn 0.23.2
Pandas 1.1.1

Training and testing

Please run following commands for training and testing. We take the dataset example as the example.

Event prediction

python:

python train_event_predictor.py --runs 5 --dp ../data/ --gpu 1 -d example --seq-len 7

Acknowledgements

This repo is based on Glean [3]. Great thanks to the original authors for their work!

Cite

Please cite our paper if you find this code useful for your research.

References

[1] Kalev Leetaru and Philip A. Schrodt. 2013. GDELT: Global data on events, location, and tone, 1979-2012. ISA Annual Convention, 2(4): 1-49.

[2] Elizabeth Boschee, Jennifer Lautenschlager, Sean O’Brien, Steve Shell-man, James Starz, and Michael Ward. 2015. ICEWS coded event data. Harvard Dataverse.

[3] Songgaojun Deng, Rangwala Huzefa, and Ning Yue. 2020. Dynamic knowledge graph based multi-event forecasting. In KDD, 1585-1595.