OntoGPT

Introduction

OntoGPT is a Python package for extracting structured information from text with large language models (LLMs), instruction prompts, and ontology-based grounding.

Two different strategies for knowledge extraction are currently implemented in OntoGPT:

For more details, please see the full documentation.

Quick Start

OntoGPT runs on the command line, though there's also a minimal web app interface (see Web Application section below).

Ensure you have Python 3.9 or greater installed.
Install with pip:
```
pip install ontogpt
```

Set your OpenAI API key:

runoak set-apikey -e openai <your openai api key>

See the list of all OntoGPT commands:
```
ontogpt --help
```
Try a simple example of information extraction:
```
echo "One treatment for high blood pressure is carvedilol." > example.txt
ontogpt extract -i example.txt -t drug
```
OntoGPT will retrieve the necessary ontologies and output results to the command line. Your output will provide all extracted objects under the heading extracted_object.

Web Application

There is a bare bones web application for running OntoGPT and viewing results.

First, install the required dependencies with pip by running the following command:

pip install ontogpt[web]

Then run this command to start the web application:

web-ontogpt

NOTE: We do not recommend hosting this webapp publicly without authentication.

Evaluations

OpenAI's functions have been evaluated on test data. Please see the full documentation for details on these evaluations and how to reproduce them.

Citation

The information extraction approach used in OntoGPT, SPIRES, is described further in: Caufield JH, Hegde H, Emonet V, Harris NL, Joachimiak MP, Matentzoglu N, et al. Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning. arXiv publication: http://arxiv.org/abs/2304.02711

The gene summarization approach used in OntoGPT, SPINDOCTOR, is described further in: Joachimiak MP, Caufield JH, Harris NL, Kim H, Mungall CJ. Gene Set Summarization using Large Language Models. arXiv publication: http://arxiv.org/abs/2305.13338

Acknowledgements

This project is part of the Monarch Initiative. We also gratefully acknowledge Bosch Research for their support of this research project.