Angelo Antonio Salatino's profile document
Description for Angelo Antonio Salatino
Angelo Antonio Salatino
Angelo Antonio Salatino
Angelo Antonio
Salatino
Research Fellow
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Dr. Angelo Salatino is a Research Fellow at the Scholarly Knowledge (SKM) 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).
He is currently working on several projects, including: i) new technologies for classifying scientific papers according to their relevant research topics, ii) automatic development of ontologies of research areas, iii) novel tools for assessing the research landscape.
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 Cagliari (IT), Trento (IT), Universidad Carlos III de Madrid (ES), and Dresden (DE).
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The Open University account for Angelo Antonio Salatino
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Angelo Antonio Salatino's membership at KMi
Angelo Antonio Salatino on Facebook
Angelo Antonio Salatino on LinkedIn
Angelo Antonio Salatino on SlideShare
@angelosalatino (Angelo Antonio Salatino on Twitter)
Angelo Antonio Salatino's participation in Garden Monitor
Garden Monitor
Garden Monitor
Using data and Artificial Intelligence for efficient garden monitoring
Angelo Antonio Salatino's participation in Rexplore
Rexplore
Rexplore
2012-11-01
Exploring Research Data
Rexplore leverages novel solutions in large-scale data mining, semantic technologies and visual analytics, to provide an innovative environment for exploring and making sense of scholarly data. In particular, Rexplore allows users:
To detect and make sense of important trends in research, such as, significant migrations of researchers from one area to another, the emergence of new topics, the evolution of communities within a particular area, and several others.
To identify a variety of interesting relations between researchers, e.g., recognizing authors who share similar research trajectories. These relations go well beyond the standard co-authorship links or relationships informed by social networks, which are commonly found in other systems.
To perform fine-grained expert search with respect to detailed multi-dimensional parameters.
To analyse research performance at different levels of abstraction, including individual researchers, organizations, countries, and research communities identified on the basis of dynamic criteria.
An important aspect of Rexplore is that it does not rely on manually-generated taxonomies of research areas, which tend to be shallow and date very rapidly, but uses instead an innovative ontology population algorithm, Klink, which automatically constructs a semantic network of fine-grained research areas, linked by semantic relations, such as sameAs and subAreaOf. The use of Klink ensures a fine-grained handling of research areas and affords Rexplore a very high level of precision and recall in associating topics to publications and researchers.
Rexplore offers an advanced graphical interface, comprising a variety of innovative and fine grained visualizations, which support users in exploring authors, topics, and research communities. To support effective exploration, all graphical elements can be clicked on, thus enabling a seamless and contextualized navigation.
Angelo Antonio Salatino's participation in Supporting Editorial Activities at Springer Nature
Supporting Editorial Activities at Springer Nature
Supporting Editorial Activities at Springer Nature
2018-05-01
2021-01-31
Supporting Editorial Activities at Springer Nature
The project aims at fostering Springer Nature editorial activities by supporting them with a variety of smart solutions leveraging artificial intelligence, data mining, and semantic technologies. In particular, the KMi team will support Springer Nature editorial team in classifying proceedings and other editorial products, taking informed decisions about their marketing strategy, and improve their internal classification.
The main objectives of the project are:
- Producing several analytics solutions for the analysis of big scholarly data.
- Automatically generating a large-scale ontology describing research topics in the field of Engineering.
- Enhancing the Smart Topics Miner, a tool developed to support the Springer Nature editorial team in classifying proceedings.
- Releasing the Computer Science Ontology, the largest ontology of research areas in the field of Computer Science, which currently includes about 15K topics and 70K semantic relationships.
Angelo Antonio Salatino's participation in Augur
Augur
Augur
2014-12-01
EARLY FORECASTING OF RESEARCH TRENDS
Augur is a novel approach to the early detection of research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics.
Angelo Antonio Salatino's participation in AIDA Dashboard
AIDA Dashboard
AIDA Dashboard
2019-01-01
Assess Journals and Conferences at Springer Nature
Scientific conferences and journals play a crucial role by promoting the cross-pollination of ideas and technologies, fostering new collaborations, shaping scientific communities, and connecting research efforts from academia and industry. However, bibliometric systems and academic search engines provide a limited support for analysing scientific venues in similar fields, and to analyse the involvement of industrial sectors. This led to the creation of the AIDA Dashboard, an innovative tool for exploring and making sense of scientific venues which integrates statistical analysis, semantic technologies, and visual analytics.