From GPT to Mistral: Cross-Domain Ontology Learning with NeOn-GPT

We present the extended NeOn-GPT pipeline, an LLM-powered ontology learning pipeline grounded in the NeOn methodology. NeOn-GPT is a domain-agnostic ontology learning pipeline that comprises two components: (i) ontology draft generation: a multi-step prompting pipeline following the NeOn methodology, including requirement specification, Competency Questions generation, ontology conceptualization and implementation, formal modelling, population, documentation, (ii) automated ontology verification and resolution achieved through orchestrated calls to third-party tools complemented by LLM-suggested repairs. The extended pipeline incorporates an explicit step for reusing existing relevant domain ontologies to guide LLMs toward more consistent modeling decisions. We evaluate NeOn-GPT across four distinct 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 using three complementary metrics: structural metrics (class, property, and axiom profiles), lexical metrics (exact matches and Jaro-Winkler similarity ≥ 0.8), and semantic metrics based on sentence-transformer embeddings. Results show that LLMs consistently generate ontologies with rich relational structures (including functional, transitive, and domain-range constraints) and meaningful semantic alignment, with most entity and triple similarities falling in the 0.5-0.8 range. Overall, this study provides a comprehensive, cross-domain evaluation of a NeOn-guided LLM ontology learning pipeline, clarifying its capabilities and limitations.