HittER generates embeddings for knowledge graphs and performs link prediction using a hierarchical Transformer model. It will appear in EMNLP 2021 (arXiv version).
The repo requires python>=3.7, anaconda and a new env is recommended.
conda create -n hitter python=3.7 -y # optional
conda activate hitter # optional
git clone [email protected]:microsoft/HittER.git
cd HittER
pip install -e .First download the standard benchmark datasets using the commands below. Thanks LibKGE for providing the preprocessing scripts and hosting the data.
cd data
sh download_standard.shConfigurations for the experiments are in the /config folder.
python -m kge start config/trmeh-fb15k237-best.yamlThe training process uses DataParallel in all visible GPUs by default, which can be overrode by appending --job.device cpu to the command above.
You can evaluate the trained models on dev/test set using the following commands.
python -m kge eval <saved_dir>
python -m kge test <saved_dir>Pretrained models are also released for reproducibility.
QA experiment-related data can be downloaded from the release.
git submodule update --init
cd transformers
pip install -e .Run experiments
> python hitter-bert.py --help
usage: hitter-bert.py [-h] [--dataset {fbqa,webqsp}] [--filtered] [--hitter]
[--seed SEED]
[exp_name]
positional arguments:
exp_name Name of the experiment
optional arguments:
-h, --help show this help message and exit
--dataset {fbqa,webqsp}
fbqa or webqsp
--filtered Filtered or not
--hitter Use pretrained HittER or not
--seed SEED Seed numberThis project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
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@inproceedings{chen-etal-2021-hitter,
title = "HittER: Hierarchical Transformers for Knowledge Graph Embeddings",
author = "Chen, Sanxing and Liu, Xiaodong and Gao, Jianfeng and Jiao, Jian and Zhang, Ruofei and Ji, Yangfeng",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2021",
publisher = "Association for Computational Linguistics"
}
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