Dr. Angelo Antonio Salatino

Research 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.

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

External collaborations
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).

Keywords

Semantic Web, Semantic Publishing, Data Mining, User Modelling, Knowledge Extraction, Ontologies, Scholarly Data.


Publications

Chessa, Alessandro, Fenu, Gianni,Motta, Enrico, Reforgiato Recupero, Diego,Osborne, Francesco, Salatino, Angelo and Secchi, Luca (2022). Enriching Data Lakes with Knowledge Graphs. In: Knowledge Graph Generation from Text, (In Press).

Angioni, Simone,Salatino, Angelo, Osborne, Francesco, Reforgiato Recupero, Diego and Motta, Enrico(2022). The AIDA Dashboard: a Web Application for Assessing and Comparing Scientific Conferences. IEEE Access (In press).

Angioni, Simone,Salatino, Angelo, Osborne, Francesco, Recupero, Diego Reforgiato and Motta, Enrico(2022). AIDA: a Knowledge Graph about Research Dynamics in Academia and Industry. Quantitative Science Studies, 2(4) pp. 1356–1398.

Manghi, Paolo, Mannocci, Andrea , Osborne, Francesco, Sacharidis, Dimitris,Salatino, Angelo and Vergoulis, Thanasis(2021). New Trends in Scientific Knowledge Graphs and Research Impact Assessment. Quantitative Science Studies, 2(4) pp. 1296–1300.

Angioni, Simone,Salatino, Angelo, Osborne, Francesco, Birukou, Aliaksandr,Recupero, Diego Reforgiato and Motta, Enrico(2021). Assessing Scientific Conferences through Knowledge Graphs. In: International Semantic Web Conference (ISWC) 2021: Posters, Demos, and Industry Tracks, 2980.

Angioni, Simone,Salatino, Angelo, Osborne, Francesco, Reforgiato Recupero, Diego and Motta, Enrico(2021). AIDA: a Knowledge Graph about Research Dynamics in Academia and Industry. Quantitative Science Studies (In Press).

Meloni, Antonello, Angioni, Simone,Salatino, Angelo, Osborne, Francesco, Reforgiato Recupero, Diego and Motta, Enrico(2021). AIDA-Bot: A Conversational Agent to Explore Scholarly Knowledge Graphs. In: 2021 International Semantic Web Conference Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice, CEUR WS, (In Press).

Nayyeri, Mojtaba, Müge Cil, Gökce, Vahdati, Sahar,Osborne, Francesco, Kravchenko, Andrey, Angioni, Simone,Salatino, Angelo, Reforgiato Recupero, Diego,Motta, Enrico and Lehmann, Jens(2021). Link Prediction of Weighted Triples for Knowledge Graph Completion Within the Scholarly Domain. IEEE Access, 9 pp. 116002–116014.

Salatino, Angelo, Osborne, Francesco and Motta, Enrico(2021). CSO Classifier 3.0: a scalable unsupervised method for classifying documents in terms of research topics. International Journal on Digital Libraries (Early Access).

Nayyeri, Mojtaba, Cil, Gokce Muge, Vahdati, Sahar,Osborne, Francesco, Rahman, Mahfuzur, Angioni, Simone,Salatino, Angelo, Recupero, Diego Reforgiato, Vassilyeva, Nadezhda,Motta, Enrico and Lehmann, Jens(2021). Trans4E: Link Prediction on Scholarly Knowledge Graphs. Neurocomputing (Early Access).