The main objective of this work is to show the potentialities of recently developed approaches for automatic knowledge extraction directly from the universities’ websites. The information automatically extracted can be potentially updated with a frequency higher than once per year, and be safe from manipulations or misinterpretations. Moreover, this approach allows us flexibility in collecting indicators about the efficiency of universities’ websites and their effectiveness in disseminating key contents. These new indicators can complement traditional indicators of scientific research (e.g. number of articles and number of citations) and teaching (e.g. number of students and graduates) by introducing further dimensions to allow new insights for “profiling” the analyzed universities. The main findings of this study concern the evaluation of the potential in digitalization of universities, in particular by presenting techniques for the automatic extraction of information from the web to build indicators of quality and impact of universities’ websites. These indicators can complement traditional indicators and can be used to identify groups of universities with common features using clustering techniques working with the above indicators.
2020, JOURNAL OF DATA AND INFORMATION SCIENCE, Pages 43-55 (volume: 5)
Exploring the Potentialities of Automatic Extraction of University Webometric Information (01a Articolo in rivista)
Bianchi Gianpiero, Bruni Renato, Daraio Cinzia, Laureti Palma Antonio, Perani Giulio, Scalfati Francesco
Gruppo di ricerca: Combinatorial Optimization