box-embeddings 0.1.0

Open-source library for Box Embeddings and Box Representations, built on PyTorch & TensorFlow.

Status

Installation

Installing via pip

The preferred way to install Box Embeddings is via pip. Just run pip install box-embeddings

Installing from source

You can also install Box Embeddings by cloning our git repository

git clone https://github.com/iesl/box-embeddings

Create a Python 3.7 or 3.8 virtual environment, and install Box Embeddings in editable mode by running:

pip install --editable . --user
pip install -r core_requirements.txt

Package Overview

Citing

1. If you use simple hard boxes with surrogate loss then cite the following paper:

@inproceedings{vilnis2018probabilistic,
  title={Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures},
  author={Vilnis, Luke and Li, Xiang and Murty, Shikhar and McCallum, Andrew},
  booktitle={Proceedings of the 56th Annual Meeting of the Association for
  Computational Linguistics (Volume 1: Long Papers)},
  pages={263--272},
  year={2018}
}

2. If you use softboxes without any regularizaton the cite the following paper:

@inproceedings{
li2018smoothing,
title={Smoothing the Geometry of Probabilistic Box Embeddings},
author={Xiang Li and Luke Vilnis and Dongxu Zhang and Michael Boratko and Andrew McCallum},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=H1xSNiRcF7},
}

3. If you use softboxes with regularizations defined in the Regularizations module then cite the following paper:

@inproceedings{
patel2020representing,
title={Representing Joint Hierarchies with Box Embeddings},
author={Dhruvesh Patel and Shib Sankar Dasgupta and Michael Boratko and Xiang Li and Luke Vilnis
and Andrew McCallum},
booktitle={Automated Knowledge Base Construction},
year={2020},
url={https://openreview.net/forum?id=J246NSqR_l}
}

4. If you use Gumbel box then cite the following paper:

@article{dasgupta2020improving,
  title={Improving Local Identifiability in Probabilistic Box Embeddings},
  author={Dasgupta, Shib Sankar and Boratko, Michael and Zhang, Dongxu and Vilnis, Luke
  and Li, Xiang Lorraine and McCallum, Andrew},
  journal={arXiv preprint arXiv:2010.04831},
  year={2020}
}

The code for this library can be found here.

Contributors

• Dhruvesh Patel @dhruvdcoder

• Shib Sankar Dasgupta @ssdasgupta

• Michael Boratko @mboratko

• Xiang (Lorraine) Li @Lorraine333

• Trang Tran @trangtran72

• Purujit Goyal @purujitgoyal

• Tejas Chheda @tejas4888

Contributions

We welcome all contributions from the community to make Box Embeddings a better package. If you're a first time contributor, we recommend you start by reading our CONTRIBUTING.md guide.

Team

Box Embeddings is an open-source project developed by the research team from the Information Extraction and Synthesis Laboratory at the College of Information and Computer Sciences (UMass Amherst).