Dr Angelo Salatino
Research Fellow
Biography
Professional biography
Dr. Angelo Salatino is a Research Fellow at the Scholarly Knowledge team (SKM3), at the Knowledge Media Institute (KMi) of the Open University. He obtained a Ph.D., studying methods for the early detection of research trends. In particular, his project aimed at identifying the emergence of new research topics at their embryonic stage (i.e., before being recognised by the research community).
Currently, he is mainly working on: i) new technologies for classifying scientific papers according to their relevant research topics, and ii) how the research output of academia fosters innovation in the industry.
Research interests
His research interests are in the areas of Semantic Web, Network Science and Knowledge Discovery technologies, with focus on the structure and evolution of science: Science of Science
Publications
Book Chapter
Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs (2021)
Ontology Extraction and Usage in the Scholarly Knowledge Domain (2020)
Journal Article
A Survey on Knowledge Organization Systems of Research Fields: Resources and Challenges (2025)
Artificial intelligence for literature reviews: opportunities and challenges (2024)
Data-Driven Methodology for Knowledge Graph Generation Within the Tourism Domain (2023)
Integrating Conversational Agents and Knowledge Graphs Within the Scholarly Domain (2023)
AIDA: a Knowledge Graph about Research Dynamics in Academia and Industry (2022)
The AIDA Dashboard: a Web Application for Assessing and Comparing Scientific Conferences (2022)
R-classify: Extracting research papers’ relevant concepts from a controlled vocabulary (2022)
New Trends in Scientific Knowledge Graphs and Research Impact Assessment (2021)
Trans4E: Link Prediction on Scholarly Knowledge Graphs (2021)
How are topics born? Understanding the research dynamics preceding the emergence of new areas (2017)
Presentation / Conference
Capturing the Viewpoint Dynamics in the News Domain (2024)
Classifying Scientific Topic Relationships with SciBERT (2024)
Leveraging Language Models for Generating Ontologies of Research Topics (2024)
Enriching Data Lakes with Knowledge Graphs (2022)
Leveraging Knowledge Graph Technologies to Assess Journals and Conferences at Springer Nature (2022)
Assessing Scientific Conferences through Knowledge Graphs (2021)
AIDA-Bot: A Conversational Agent to ExploreScholarly Knowledge Graphs (2021)
The AIDA Dashboard: Analysing Conferences with Semantic Technologies (2020)
ResearchFlow: Understanding the Knowledge Flow between Academia and Industry (2020)
Integrating Knowledge Graphs for Analysing Academia and Industry Dynamics (2020)
Improving Editorial Workflow and Metadata Quality at Springer Nature (2019)
Smart Topics Miner 2: Improving Proceedings Retrievability at Springer Nature (2019)
Integrating Knowledge Graphs for Comparing the Scientific Output of Academia and Industry (2019)
The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles (2019)
Classifying Research Papers with the Computer Science Ontology (2018)
The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas (2018)
AUGUR: Forecasting the Emergence of New Research Topics (2018)
2100 AI: Reflections on the mechanisation of scientific discovery (2017)
Smart Book Recommender: A Semantic Recommendation Engine for Editorial Products (2017)
Supporting Springer Nature Editors by means of Semantic Technologies (2017)
Detection of Embryonic Research Topics by Analysing Semantic Topic Networks (2016)
Smart Topic Miner: Supporting Springer Nature Editors with Semantic Web Technologies (2016)
Automatic Classification of Springer Nature Proceedings with Smart Topic Miner (2016)
