Angelo Antonio SalatinoResearch Associate
Dr. Angelo Salatino is a Research Associate at the Intelligence Systems and Data Science (ISDS) group, 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.
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
Angelo collaborates with a number of academic and industrial partners. In particular, he collaborates with Spinger Nature, the world's largest academic book publisher, as well as universities in Georgetown (USA), Trento (IT), Cagliari (IT), Oxford (UK), and FIZ Karlsruhe (DE).
Keys: Semantic Web, Semantic Publishing, Data Mining, User Modelling, Knowledge Extraction, Ontologies, Scholarly Data.
08 Mar 2021
23 Feb 2021
11 Jan 2021
29 Oct 2020
10 Sep 2020
Meloni, A., Angioni, S., Salatino, A.A., Osborne, F., Recupero, D. and Motta, E. (2021) AIDA-Bot: A Conversational Agent to ExploreScholarly Knowledge Graphs, The International Semantic Web Conference, Online
Angioni, S., Salatino, A.A., Osborne, F., Birukou, A., Recupero, D. and Motta, E. (2021) Assessing Scientific Conferences through Knowledge Graphs, Proceedings of the ISWC 2021 Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice, Virtual, Online
Nayyeri, M., Cil, G., Vahdati, S., Osborne, F., Kravchenko, A., Angioni, S., Salatino, A.A., Recupero, D., Motta, E. and Lehmann, J. (2021) Link Prediction of Weighted Triples for Knowledge Graph Completion Within the Scholarly Domain, IEEE Access, 9, pp. 116002-116014
Salatino, A.A., Osborne, F. and Motta, E. (2021) CSO Classifier 3.0: a scalable unsupervised method for classifying documents in terms of research topics, International Journal on Digital Libraries, pp. (Early Access)
Salatino, A.A., Mannocci, A. and Osborne, F. (2021) Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs Prediction Dynamics of Research Impact, eds. Yannis Manolopoulos,Thanasis Vergoulis, Springer (In press)
Early Detection and Forecasting of Research Trends
Techreport ID: kmi-15-02
Author(s): Angelo Antonio Salatino