Departmental Seminar – “Sprinkling Semantics and AI into Metascience”
| Dates: | 4 March 2026 |
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| Times: | 14:00 - 15:00 |
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| What is it: | Seminar |
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| Organiser: | Department of Computer Science |
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| How much: | Free |
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| Who is it for: | University staff, Current University students |
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| Speaker: | Dr Angelo Salatino |
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This talk explores the role of Artificial Intelligence and Semantic Web technologies in the field of metascience, focusing on how these computational methods can be used to better understand and optimise the research ecosystem. While traditional methods of categorising and analysing scientific progress often rely on manual curation that is difficult to scale, we introduce innovative AI-driven frameworks designed to automatically generate fine-grained representations of research knowledge. By leveraging large-scale ontologies and machine learning models, our work facilitates the tracking of research trends, the identification of knowledge flows between academia and industry, and the acceleration of progress through automated literature synthesis. Ultimately, this research aims to provide evidence-based insights that can inform funding strategies and national research policies, ensuring that transformative ideas are identified and supported more effectively.
Speaker
Dr Angelo Salatino
Role: Research Fellow at the Knowledge Media Institute
Organisation: The Open University
Biography: Dr Angelo Salatino is a Research Fellow at the Knowledge Media Institute of The Open University who develops AI solutions to analyse scholarly data. His main research interests include detecting emerging research trends and creating semantic technologies to organise scholarly knowledge. He has created several systems used by publishers and research organisations, including the Computer Science Ontology (CSO), currently the largest taxonomy of research topics in Computer Science; the CSO Classifier, which annotates research documents; and Augur, a tool for detecting emerging research topics. Recently, his research has focused on investigating how Large Language Models can support the analysis of scientific knowledge. He is particularly interested in exploring how these models can be enhanced with domain knowledge to understand scientific content better and support researchers and practitioners in their daily activities.
Travel and Contact Information
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Kilburn_TH 1.3
Kilburn Building
Manchester
