Benchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical Field
Knowledge Organization Systems like Ontologies and taxonomies are fundamental for structuring scientific knowledge, yet their manual curation presents a persistent bottleneck in knowledge management. While Large Language Models (LLMs) offer a scalable mechanism for automated ontology generation, their capacity to classify complex, domain-specific semantics requires systematic evaluation. In this paper, we assess the performance of five small, open-source LLMs (up to 9 billion parameters) in identifying semantic relationships between biomedical concepts. To support this evaluation, we introduce MeSH-Rel-4K, a dataset comprising 4K semantic relationships extracted from the Medical Subject H...
| Authors | Tanay Aggarwal, Angelo Salatino, Francesco Osborne et al. |
|---|---|
| Primary category | cs.DL |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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