IBM / gnn-taxo-construction

A tool for taxonomy construction using Graph Neural Networks (GNN).

Graph2Taxo

Graph2Taxo is a GNN-based cross-domain transfer framework for taxonomy construction. It uses a noisy graph constructed from automatically extracted noisy hyponym-hypernym candidate pairs, and a set of taxonomies for some known domains for training. The learned model is then used to generate taxonomy for a new unknown domain given a set of terms for that domain.

If you use this system, please cite the following paper -

@inproceedings{chao2020-g2t,
    title={Taxonomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer},
    author={Chao Shang and Sarthak Dash and Md Faisal Mahbub Chowdhury and Nandana Mihindukulasooriya and Alfio Gliozzo},
    booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020) },
    publisher = "Association for Computational Linguistics",
    year      = {2020},
}

Installation

Install PyTorch from the official website or using Anaconda.

Initializing Git submodules.

After cloning the repo, if you need to process the data, please use the command git submodule update to initialize the dependent submodules. This will clone TaxoRL and TAXI projects that are used to reproduce data from existing experiments.

git submodule update

Train model

Dataset

TAXI data is given in the "data/TAXI_dataset" folder. Data from TaxoRL paper is given in the "data/TaxoRL_dataset" folder.

When you process the data, you can run:

python preprocess.py

Train model

When you train the model, you can run:

python train.py

Design your own model

You can directly modify the GRAPH2TAXO model in the "models.py" file.

Acknowledgments

GCN, TaxoRL, TAXI and SACN.