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

• URL: http://arxiv.org/abs/2508.20693v1
• Date: Thu, 28 Aug 2025 11:53:45 GMT
• Title: Leveraging Large Language Models for Generating Research Topic Ontologies: A Multi-Disciplinary Study
• Authors: Tanay Aggarwal, Angelo Salatino, Francesco Osborne, Enrico Motta,
• Abstract summary: We investigate the capability of several large language models to identify semantic relationships among research topics.<n>Our experiments demonstrate that fine-tuning Mess on PEM-Rel-8K yields excellent performance across all disciplines.
• Score: 0.8633013637160062
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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-domain 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 domains: 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-domain transferability of fine-tuned models by measuring their performance when trained in one domain 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.

Related papers

• WildSci: Advancing Scientific Reasoning from In-the-Wild Literature [50.16160754134139]
  We introduce WildSci, a new dataset of domain-specific science questions automatically synthesized from peer-reviewed literature.<n>By framing complex scientific reasoning tasks in a multiple-choice format, we enable scalable training with well-defined reward signals.<n>Experiments on a suite of scientific benchmarks demonstrate the effectiveness of our dataset and approach.
  arXiv  Detail & Related papers  (2026-01-09T06:35:23Z)
• A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers [251.23085679210206]
  Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research.<n>This survey reframes the development of Sci-LLMs as a co-evolution between models and their underlying data substrate.<n>We formulate a unified taxonomy of scientific data and a hierarchical model of scientific knowledge.
  arXiv  Detail & Related papers  (2025-08-28T18:30:52Z)
• ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows [82.07367406991678]
  Large Language Models (LLMs) have extended their impact beyond Natural Language Processing.<n>Among these, computer-using agents are capable of interacting with operating systems as humans do.<n>We introduce ScienceBoard, which encompasses a realistic, multi-domain environment featuring dynamic and visually rich scientific software.
  arXiv  Detail & Related papers  (2025-05-26T12:27:27Z)
• Assessing the Capability of Large Language Models for Domain-Specific Ontology Generation [1.099532646524593]
  Large Language Models (LLMs) have shown significant potential for ontology engineering.<n>We investigate the generalizability of two state-of-the-art LLMs, DeepSeek and o1-preview, by generating from a set of competency questions.<n>Our findings show that the performance of the experiments is remarkably consistent across all domains.
  arXiv  Detail & Related papers  (2025-04-24T09:47:14Z)
• Biological Sequence with Language Model Prompting: A Survey [14.270959261105968]
  Large Language models (LLMs) have emerged as powerful tools for addressing challenges across diverse domains.<n>This paper systematically investigates the application of prompt-based methods with LLMs to biological sequences.
  arXiv  Detail & Related papers  (2025-03-06T06:28:36Z)
• Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field [0.0]
  This paper offers an analysis of the ability of large models to identify semantic relationships between different research topics.<n>We developed a gold standard based on the IEEE Thesaurus to evaluate the task.<n>Several models have achieved outstanding results, including Mixtral-8x7B, Dolphin-Mistral, and Claude 3-7B.
  arXiv  Detail & Related papers  (2024-12-11T10:11:41Z)
• Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
  Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
  arXiv  Detail & Related papers  (2024-07-17T20:01:21Z)
• Ontology Embedding: A Survey of Methods, Applications and Resources [54.3453925775069]
  Onologies are widely used for representing domain knowledge and meta data.<n> logical reasoning that can directly support are quite limited in learning, approximation and prediction.<n>One straightforward solution is to integrate statistical analysis and machine learning.
  arXiv  Detail & Related papers  (2024-06-16T14:49:19Z)
• A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery [68.48094108571432]
  Large language models (LLMs) have revolutionized the way text and other modalities of data are handled. We aim to provide a more holistic view of the research landscape by unveiling cross-field and cross-modal connections between scientific LLMs.
  arXiv  Detail & Related papers  (2024-06-16T08:03:24Z)
• Evaluating the state-of-the-art in mapping research spaces: a Brazilian case study [0.0]
  Two recent works propose methods for creating research maps from scientists' publication records. We evaluate these models' ability to predict whether a given entity will enter a new field. We conduct a case study to showcase how these models can be used to characterize science dynamics in the context of Brazil.
  arXiv  Detail & Related papers  (2021-04-07T18:14:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.