SBU-NLP at LLMs4OL 2025 Tasks A, B, and C: Stage-Wise Ontology Construction Through LLMs Without any Training Procedure

Abstract

Automated ontology construction is a challenging task that traditionally requires extensive domain expertise, data preprocessing, and resource-intensive model training. While learning-based methods with fine-tuning are common, they often suffer from high computational costs and limited generalizability across domains. This paper explores a fully automated approach that leverages powerful large language models (LLMs) through prompt engineering, eliminating the need for training or fine-tuning. We participated in the LLMs4OL 2025 shared task, which includes four subtasks: extracting ontological terms and types (Text2Onto), assigning generalized types to terms (Term Typing), discovering taxonomic relations (Taxonomy Discovery), and extracting non-taxonomic semantic relations (Non-Taxonomic Relation Extraction). Our team focused on the first three tasks by using stratified random sampling, simple random sampling, and chunking-based strategies to include training sets in the prompts without limitations imposed by context window sizes. This simple yet general approach has proven effective across these tasks, enabling high-quality ontology construction without additional annotations or training. Additionally, we show that pretrained sentence embedding models ranging from 0.1B to 1.5B parameters perform comparably to a simple F1 token overlap baseline in taxonomy discovery, suggesting that embedding-based methods may not always offer significant advantages. Our findings highlight that prompt-based strategies with modern LLMs enable efficient, scalable, and domain-independent ontology construction, providing a promising alternative to traditional, resource-heavy methods.