OntoChimp is a collaborative project focused automated methods to expedite ontology development, using AI resources such as ChatGPT to identify key concepts and clustered terms in a curated set of documents covering the scope of a new domain, with additional resources to "bucketize" the terms identified into groups that will allow subject matter experts to assess similar terms together. The system also aids in identifying new key concepts in existing ontologies.
- Document Analysis using ChatGPT for Key Concept and Clustered Terms identificaiton.
- Methods for categorizing or "bucketizing" (grouping similar terms without ontological categorization) the terms identified.
- Automated methods to validate the terms identified, for instance by matching each term to a set of ontologies using the BioPortal Annotator and Search API's.
- Initiated during the Helab PHASES zoom of 7/21/26, adapting the OntoChimp desktop Python app for web access
- Many programs have changed due to the addition of a multi-model capability to access up to 3 LLM's, and the use of SQLite database to accommodate the multi-model operation, replacing much of the JSON formatted TermTable.
git clone https://github.com/yourusername/OntoChimp.git
cd OntoChimp
pip install -r requirements.txt
python main.py --help
Contributing
See CONTRIBUTING.md for collaboration rules and code guidelines.
License
MIT (or whichever license you choose)Top Two Folders : The main folders - one for code and one for project data
βββ OntoChimp Folder for app code
β βββ src Source code (desktop, not in frontend or backend)
β βββ models Models and classes, eg class_TermTable
β βββ utils Utility function modules
βββ OntoChimp_Projects Projects using OntoChimp
βββ Proj1003_PHASES Proj1003_PHASES etc. - main project level data
βββ data PHASES data
βββ refDocs Reference documents, text versions, refDoc.yaml files
βββ results_chatgpt Output Workbooks from ChatGPT access
βββ results_combined Output of Combiner App
βββ results_key_terms Output of TermTable and other key term data