Extended NeOn-GPT: Advancing LLM-Powered Ontology Learning Through Ontology Reuse and Automated Verification

We present the extended NeOn-GPT pipeline, an LLM-powered, domain-agnostic ontology learning framework grounded in the NeOn methodology. The pipeline comprises two components: (i) ontology draft generation through multi-step prompting—covering requirement specification, competency questions, conceptualization, formal modeling, population, and documentation—and (ii) automated verification and repair through orchestrated calls to third-party tools complemented by LLM-suggested fixes. The extended pipeline introduces an explicit ontology reuse step to guide LLMs toward more consistent modeling decisions. We evaluate NeOn-GPT across four domains (Wine, Cheminformatics, Environmental Microbiology, and Sewer Networks) using both proprietary (GPT-4o) and open-source (Mistral, Llama-4, DeepSeek) models. Gold-standard alignment is assessed via structural metrics (class, property, and axiom profiles), lexical metrics, and semantic metrics based on sentence embeddings. Results show that LLMs consistently generate ontologies with rich relational structures and meaningful semantic alignment, with most entity and triple similarities falling in the 0.5–0.8 range. This study provides a comprehensive, cross-domain evaluation of NeOn-guided LLM ontology learning, clarifying its capabilities and limitations.