This is the code for the manuscript "TempoQR: Temporal Question Reasoning over Knowledge Graphs" (AAAI2022). Paper: https://arxiv.org/abs/2112.05785
Clone and create a conda environment
git clone https://github.com/cmavro/TempoQR.git
cd TempoQR
conda create --prefix ./tempoqr_env python=3.7
conda activate ./tempoqr_env
The implementation is based on CronKGQA in Question Answering over Temporal Knowledge Graphs and their code from https://github.com/apoorvumang/CronKGQA. You can find more installation details there. We use TComplEx KG Embeddings as implemented in https://github.com/facebookresearch/tkbc.
Install TempoQR requirements
conda install --file requirements.txt -c conda-forge
Download and unzip data.zip and models.zip in the root directory.
Drive: https://drive.google.com/drive/folders/1aS2s5sZ0qlDpGZ9rdR7HcHym23N3pUea?usp=sharing.
TempoQR:
python ./train_qa_model.py --model tempoqr --supervision soft
python ./train_qa_model.py --model tempoqr --supervision hard
Other models: "entityqr" and "cronkgqa" with hard and soft supervisions.
To use a corrupted TKG change to "--tkg_file train_corXX.txt" and "--tkbc_model_file tcomplex_corXX.ckpt", where XX=20,33,50.
To evaluate on unseen complex questions change to "--test test_bef_and_aft" or "--test test_fir_las_bef_aft".
Please explore more argument options in train_qa_model.py.
Minor Note: Not all modules have been tested after the code merging.
If you find our method, code, or experimental setups useful, please cite our paper:
@misc{mavromatis2021tempoqr,
title={TempoQR: Temporal Question Reasoning over Knowledge Graphs},
author={Costas Mavromatis and Prasanna Lakkur Subramanyam and Vassilis N. Ioannidis and Soji Adeshina and Phillip R. Howard and Tetiana Grinberg and Nagib Hakim and George Karypis},
year={2021},
eprint={2112.05785},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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