CUET_Zenith at LLMs4OL 2025 Task C: Hybrid Embedding-LLM Architectures for Taxonomy Discovery

Abstract

Taxonomy discovery, the identification of hierarchical relationships within ontological structures, constitutes a foundational challenge in ontology learning. Our submission to the LLMs4OL 2025 challenge, employing hybrid architectures to address this task across both biomedical (Subtask C1: OBI) and general-purpose (Subtask C5: SchemaOrg) knowledge domains. For C1, we have integrated semantic clustering of Sentence-BERT embeddings with few-shot prompting using Qwen-3 (14B), enabling domain-specific hierarchy induction without task-specific fine-tuning. For C5, we have introduced a cascaded validation framework, harmonizing deep semantic representations from sentence transformer {all-mpnet-base-v2}, ensemble classification via XGBoost, and a hierarchical LLM-based reasoning pipeline utilizing TinyLlama and GPT-4o. To address inherent class imbalances, we have employed SMOTE-based augmentation and gated inference thresholds. Empirical results demonstrate that our hybrid methodology achieves competitive performance, confirming that the judicious integration of classical machine learning with large language models yields efficient and scalable solutions for ontology structure induction. Code implementations are publicly available.