# Leveraging Large Language Models for Generating Research Topic Ontologies: A Multi-Disciplinary Study

arXiv: https://arxiv.org/abs/2508.20693 · HTML: https://mlsanity.com/paper/2508.20693/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 70 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Gemma: Open Models Based on Gemini Research and Technology](https://mlsanity.com/paper/2403.08295/) (2024)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [LLMs4OL: Large Language Models for Ontology Learning](https://mlsanity.com/paper/2307.16648/) (2023)
- [A Web-scale system for scientific knowledge exploration](https://mlsanity.com/paper/1805.12216/) (2018)
- [Artificial Intelligence for Literature Reviews: Opportunities and Challenges](https://mlsanity.com/paper/2402.08565/) (2024)
- [Constructing Taxonomies from Pretrained Language Models](https://mlsanity.com/paper/2010.12813/) (2020)
- [The emergence of Large Language Models (LLM) as a tool in literature reviews: an LLM automated systematic review](https://mlsanity.com/paper/2409.04600/) (2024)
- [Are Large Language Models a Good Replacement of Taxonomies?](https://mlsanity.com/paper/2406.11131/) (2024)
- [HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction](https://mlsanity.com/paper/2410.09112/) (2024)
- [LLMs4Life: Large Language Models for Ontology Learning in Life Sciences](https://mlsanity.com/paper/2412.02035/) (2024)
- [Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field](https://mlsanity.com/paper/2412.08258/) (2024)
- [A Survey on Knowledge Organization Systems of Research Fields: Resources and Challenges](https://mlsanity.com/paper/2409.04432/) (2024)

## Built on by (most cited first, 2 of 2)

- [OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs](https://mlsanity.com/paper/2607.17963/) (2026)
- [Research Entity Extraction and Topic Detection from UKRI Grant Proposals](https://mlsanity.com/paper/2606.30304/) (2026)

## Abstract (arXiv)

Ontologies and taxonomies of research fields are critical for managing and organising scientific knowledge, as they facilitate efficient classification, dissemination and retrieval of information. However, the creation and maintenance of such ontologies are expensive and time-consuming tasks, usually requiring the coordinated effort of multiple domain experts. Consequently, ontologies in this space often exhibit uneven coverage across different disciplines, limited inter-discipline connectivity, and infrequent updating cycles. In this study, we investigate the capability of several large language models to identify semantic relationships among research topics within three academic disciplines: biomedicine, physics, and engineering. The models were evaluated under three distinct conditions: zero-shot prompting, chain-of-thought prompting, and fine-tuning on existing ontologies. Additionally, we assessed the cross-discipline transferability of fine-tuned models by measuring their performance when trained in one discipline and subsequently applied to a different one. To support this analysis, we introduce PEM-Rel-8K, a novel dataset consisting of over 8,000 relationships extracted from the most widely adopted taxonomies in the three disciplines considered in this study: MeSH, PhySH, and IEEE. Our experiments demonstrate that fine-tuning LLMs on PEM-Rel-8K yields excellent performance across all disciplines.