1 citations · 3 across the 4 of their papers we have counts for
5 papers
Benchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical Field
Tanay Aggarwal, Angelo Salatino, Francesco Osborne +1
Knowledge Organization Systems like Ontologies and taxonomies are fundamental for structuring scientific knowledge, yet their manual curation presents a persistent bottleneck in kn…
Leveraging Large Language Models for Generating Research Topic Ontologies: A Multi-Disciplinary Study
Tanay Aggarwal, Angelo Salatino, Francesco Osborne +1
Ontologies and taxonomies of research fields are critical for managing and organising scientific knowledge, as they facilitate efficient classification, dissemination and retrieval…
How Do LLMs Encode Scientific Quality? An Empirical Study Using Monosemantic Features from Sparse Autoencoders
Michael McCoubrey, Angelo Salatino, Francesco Osborne +1
In recent years, there has been a growing use of generative AI, and large language models (LLMs) in particular, to support both the assessment and generation of scientific work. Al…
Modelling and Classifying the Components of a Literature Review
Francisco Bolaños, Angelo Salatino, Francesco Osborne +1
Previous work has demonstrated that AI methods for analysing scientific literature benefit significantly from annotating sentences in papers according to their rhetorical roles, su…
A Hybrid AI Methodology for Generating Ontologies of Research Topics from Scientific Paper Corpora
Alessia Pisu, Livio Pompianu, Francesco Osborne +3
Taxonomies and ontologies of research topics (e.g., MeSH, UMLS, CSO, NLM) play a central role in providing the primary framework through which intelligent systems can explore and i…