silp_nlp at LLMs4OL 2025 Tasks A, B, C, and D: Clustering-Based Ontology Learning Using LLMs

Abstract

This paper presents the participation of the silp\_nlp team in the LLMs4OL 2025 Challenge, where we addressed four core tasks in ontology learning: Text2Onto (Task A), Term Typing (Task B), Taxonomy Discovery (Task C), and Non-Taxonomic Relation Extraction (Task D). Building on our experience from the first edition, we proposed a clustering-enhanced methodology grounded in large language models (LLMs), integrating domain-adapted transformer models such as pranav-s/MaterialsBERT, dmis-lab/biobert-v1.1, and proprietary LLMs from Grok. Our framework combined lexical and semantic clustering with adaptive prompting to tackle entity and type extraction, semantic classification, hierarchical structure discovery, and complex relation modeling. Experimental results across 18 subtasks highlight the strength of our approach, particularly in blind and zero-shot scenarios. Notably, our model achieved multiple first-rank scores in taxonomy discovery and non-taxonomic relation extraction subtasks, validating the efficacy of clustering when coupled with semantically specialized LLMs. This work demonstrates that clustering-driven, LLM-based approaches can advance robust and scalable ontology learning across diverse domains.