Decompressing Knowledge Graph Representations for Link Prediction

This is the code we used in our paper

Decompressing Knowledge Graph Representations for Link Prediction

Xiang Kong*, Xianyang Chen*, Eduard Hovy (*: equal contribution)

Requirements

Python 3

PyTorch >= 1.0

Installation

This repo supports Linux and Python installation via Anaconda.

1. Install the requirements pip install -r requirements.txt
2. Download the default English model used by spaCy, which is installed in the previous step python -m spacy download en
3. Run the preprocessing script for WN18RR, FB15k-237: sh preprocess.sh

Running a model

CUDA_VISIBLE_DEVICES=0 python main.py model DistMultDecompress dataset FB15k-237 lr  0.001 hidden_drop 0.2 epochs 30

will run a model DistMultDecompress on FB15k-237. More models are listed in model.py

ToDo: right now, only models on FB15k-237 are uploaded and those on WN18RR will uploaded soon.

Acknowledgement

This repo is adapted from ConvE (https://github.com/TimDettmers/ConvE). Thanks!

Citation

If you found this codebase or our work useful please cite us:

@article{kong2019decompressing,
  title={Decompressing Knowledge Graph Representations for Link Prediction},
  author={Kong, Xiang and Chen, Xianyang and Hovy, Eduard},
  journal={arXiv preprint arXiv:1911.04053},
  year={2019}
}