Exploring the Potentialities of Automatic Extraction of University Webometric Information

• 作者: Gianpiero Bianchi Renato Bruni Cinzia Daraio Antonio Laureti Palma Giulio Perani Francesco Scalfati
• 来源期刊: 数据与情报科学学报:英文版
• 学科: 工学
• 关键词: Development of data and information services Webometrics indicators Higher education institutions Automatic extraction Machine learning OPTIMIZATION
• 年，卷（期）: 2020,（4）
• 页码范围: 43-55
• 页数: 13页
• 分类号: TP3

摘要

Purpose: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.Design/methodology/approach:Webometrics relies on web mining methods and techniques to perform quantitative analyses of the web.This study implements an advanced application of the webometric approach,exploiting all the three categories of web mining:web content mining;web structure mining;web usage mining.The information to compute our indicators has been extracted from the universities’websites by using web scraping and text mining techniques.The scraped information has been stored in a NoSQL DB according to a semistructured form to allow for retrieving information efficiently by text mining techniques.This provides increased flexibility in the design of new indicators,opening the door to new types of analyses.Some data have also been collected by means of batch interrogations of search engines(Bing,www.bing.com)or from a leading provider of Web analytics(SimilarWeb,http://www.similarweb.com).The information extracted from the Web has been combined with the University structural information taken from the European Tertiary Education Register(https://eter.joanneum.at/#/home),a database collecting information on Higher Education Institutions(HEIs)at European level.All the above was used to perform a clusterization of 79 Italian univ