CoA-Text2OWL: Enhancing Ontology Learning with Chain-of-Agents Framework

Ontology learning from unstructured text remains a complex challenge, particularly for large and intricate textual sources. This paper introduces CoA-Text2OWL, a multi-agent framework that leverages Large Language Models (LLMs) within a Chain-of-Agents to improve ontology generation. Unlike traditional single-LLM approaches, CoA-Text2OWL distributes the task across multiple worker agents, each processing a chunk of the input text, while a manager agent synthesizes their outputs into a coherent ontology. We evaluate our approach against a baseline single-LLM-based Text2OWL method, demonstrating improvements in object property extraction and ontology completeness. However, challenges remain in preserving hierarchical structures. Our results highlight the potential of multi-agent AI for ontology learning and suggest future enhancements, including specialized agent roles for term extraction, classification, and validation. We further validate CoA-Text2OWL by applying it to construct ontologies from real-world TRACES data related to urban systems in Geneva, achieving strong semantic alignment with source documents. This research contributes to the evolving field of LLM-powered multi-agent systems and their application in knowledge representation.