https://github.com/IBM/gnn-taxo-construction

A tool for taxonomy construction using Graph Neural Networks (GNN).
https://github.com/IBM/gnn-taxo-construction

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

• Host: GitHub
• URL: https://github.com/IBM/gnn-taxo-construction
• Owner: IBM
• License: apache-2.0
• Created: 2019-08-26T14:25:35.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-07-25T10:45:42.000Z (5 months ago)
• Last Synced: 2024-08-05T03:01:49.486Z (5 months ago)
• Language: Python
• Homepage:
• Size: 36.7 MB
• Stars: 28
• Watchers: 11
• Forks: 13
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-taxonomy - https://github.com/IBM/gnn-taxo-construction

README

        
# 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](https://pytorch.org/get-started/) 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](https://github.com/morningmoni/TaxoRL) and [TAXI](https://github.com/uhh-lt/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](https://github.com/tkipf/gcn), [TaxoRL](https://github.com/morningmoni/TaxoRL), [TAXI](https://github.com/uhh-lt/taxi) and [SACN](https://github.com/JD-AI-Research-Silicon-Valley/SACN).