LOVE: Learning Ontologies Via Embeddings

Food ontologies require significant effort to create and maintain, as it involves manual and time-consuming tasks. In this project, we propose a semi-supervised framework for automated ontology learning from an existing ontology scaffold by using word embeddings.

1. Directories

Following is a short description of each directory under the root folder.

• config: Contains all configurations files.
• data: Contains all data files.
• hpc_scripts: Scripts for running the code on HPC.
• managers: Contains all python modules.
• output: All output files go here.
• utils: Other utility files used in the project go here.

2. Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

2a. Prerequisites

In addition to Python 3.6+, you can run the following command to install the required Python libraries.

pip install -r requirements.txt

Python package pattern depends on libmysqlclient-dev. For Debian / Ubuntu, install like following.

sudo apt-get install libmysqlclient-dev

2b. Downloading Data

You need to download the GloVe 6B pre-trained weights. Following command downloads the word embeddings in GloVe format and converts them to Word2Vec compatible format.

cd root/data/pretrain
./download_convert_glove.sh

2c. Running

Configuration files use a general path /path/to/project/root/directory for compatibility. Please update these general paths to match your local computer. You can run the following script to do so.

# Update to local path.
./update_paths.sh

# You can optionally revert to the original path by running the following command.
./update_paths.sh revert

You can run all the code by running the following script. Please refer to the in-line comments of the script for details.

cd managers
python parse_foodon.py
cd ..
./run.sh

3. Authors

• Jason Youn @ https://github.com/jasonyoun
• Tarini Naravane @ https://github.com/nytarini

4. Contact

For any questions, please contact us at tagkopouloslab@ucdavis.edu.

5.Citation

Paper is under review. This section will be updated once paper is published.

6. License

This project is licensed under the GNU GPLv3 License. Please see the LICENSE file for details.

7. Acknowledgments

• We would like to thank the members of the Tagkopoulos lab for their suggestions.