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.sh

Configurations for the experiments are in the /config folder.

python -m kge start config/trmeh-fb15k237-best.yaml

The 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 number

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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"
}