Year: 2024
Type: conference-paper
Abstract: Ontology development involves a top-down approach where ontology engineers and domain experts collaboratively define and evaluate ontological elements and axioms. Translating ontology axioms into natural language can significantly aid in ontology evaluation by making the content more understandable to subject matter experts who may lack a background in knowledge engineering. In this preliminary study, we investigate the potential of large language models (LLMs) in axiom translation from ontologies to facilitate ontology evaluation. We utilize Llama 3 to translate 1,192 ontology axioms across 19 distinct axiom types from five published ontologies. Results show that 163 (13.67%) of the Llama 3 translation of the axiom are accurately represented, 268 (22.48%) are not accurately represented, and 761 (63.84%) are partially accurate. Our manual evaluation of the Llama 3 translation indicates some competency in producing hierarchical natural language equivalents while revealing some limitations when translating complex axioms. Nonetheless, there are opportunities to improve the results with few-shot training or using LLMs to provide support in knowledge engineering for ontologies.
Source: PubMed
Authors: Xubing Hao, Licong Cui, Cui Tao, Kirk Roberts, Muhammad Amith
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: AI-based conversational agent for facilitating education and communication about HPV vaccination in children and adolescents, CAREER: Advancing the Role of Ontologies for Data Science in Biomedicine, Trustworthy and High-Performance Question Answering for Electronic Health Records, Standardizing and Harmonizing Behavioral and Social Science Research Factors in Alzheimer's Disease through Ontology-Based Approaches, ACCELERATE-BASSO: Coordinating Center for Accelerating Behavioral and Social Science through Ontology, Transforming motivational interviewing into a computable model for automated patient diabetic counseling.
OpenAlex ID: W4411505803