Leveraging LLMs for Collaborative Ontology Engineering in Parkinson Disease Monitoring and Alerting

TL;DR

This study investigates how Large Language Models can assist in developing a comprehensive Parkinson's Disease monitoring ontology, emphasizing the importance of human collaboration for accuracy and completeness.

Contribution

The paper introduces hybrid LLM-human methodologies, X-HCOME and SimX-HCOME+, demonstrating improved ontology quality through collaboration.

Findings

LLMs can autonomously generate initial PD ontologies.

Human-LLM collaboration significantly enhances ontology completeness.

Iterative refinement with human oversight yields more accurate ontologies.

Abstract

This paper explores the integration of Large Language Models (LLMs) in the engineering of a Parkinson's Disease (PD) monitoring and alerting ontology through four key methodologies: One Shot (OS) prompt techniques, Chain of Thought (CoT) prompts, X-HCOME, and SimX-HCOME+. The primary objective is to determine whether LLMs alone can create comprehensive ontologies and, if not, whether human-LLM collaboration can achieve this goal. Consequently, the paper assesses the effectiveness of LLMs in automated ontology development and the enhancement achieved through human-LLM collaboration. Initial ontology generation was performed using One Shot (OS) and Chain of Thought (CoT) prompts, demonstrating the capability of LLMs to autonomously construct ontologies for PD monitoring and alerting. However, these outputs were not comprehensive and required substantial human refinement to enhance their completeness and accuracy. X-HCOME, a hybrid ontology engineering approach that combines human expertise with LLM capabilities, showed significant improvements in ontology comprehensiveness. This methodology resulted in ontologies that are very similar to those constructed by experts. Further experimentation with SimX-HCOME+, another hybrid methodology emphasizing continuous human supervision and iterative refinement, highlighted the importance of ongoing human involvement. This approach led to the creation of more comprehensive and accurate ontologies. Overall, the paper underscores the potential of human-LLM collaboration in advancing ontology engineering, particularly in complex domains like PD. The results suggest promising directions for future research, including the development of specialized GPT models for ontology construction.