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 extracte...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 universities based on structural and digital indicators.Findings: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.Research limitations:The results reported in this study refers to Italian universities only,but the approach could be extended to other university systems abroad.Practical implications:The approach proposed in this study and its illustration on Italian universities show the usefulness of recently introduced automatic data extraction and web scraping approaches and its practical relevance for characterizing and profiling the activities of universities on the basis of their websites.The approach could be applied to other university systems.Originality/value:This work applies for the first time to university websites some recently introduced techniques for automatic knowledge extraction based on web scraping,optical character recognition and nontrivial text mining operations(Bruni&Bianchi,2020).展开更多
随着可获取遥感数据的增加和网络技术的飞速发展,遥感数据的在线自动一体化服务需求日益增长,而当前尚无主流的基于服务链的遥感数据在线可视化及自动化计算平台。该文基于B/S构架,应用工作流技术,提出了一种基于服务链的集数据查询服...随着可获取遥感数据的增加和网络技术的飞速发展,遥感数据的在线自动一体化服务需求日益增长,而当前尚无主流的基于服务链的遥感数据在线可视化及自动化计算平台。该文基于B/S构架,应用工作流技术,提出了一种基于服务链的集数据查询服务、模型定制服务及信息发布服务于一体的遥感信息模型(remote sensing information models,RSIM)在线可视化定制和自动化计算的解决方案,使用户在友好的Web界面中可以一体化地按需完成从数据选择、模型设计到模型实现的动态过程。基于遥感数据各处理模块的可复用性,快速构建并实现了RSIM,是对遥感数据在线可视化定制和自动化服务的一次有益尝试。展开更多
基金This work is developed with the support of the H2020 RISIS 2 Project(No.824091)and of the“Sapienza”Research Awards No.RM1161550376E40E of 2016 and RM11916B8853C925 of 2019.This article is a largely extended version of Bianchi et al.(2019)presented at the ISSI 2019 Conference held in Rome,2–5 September 2019.
文摘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 universities based on structural and digital indicators.Findings: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.Research limitations:The results reported in this study refers to Italian universities only,but the approach could be extended to other university systems abroad.Practical implications:The approach proposed in this study and its illustration on Italian universities show the usefulness of recently introduced automatic data extraction and web scraping approaches and its practical relevance for characterizing and profiling the activities of universities on the basis of their websites.The approach could be applied to other university systems.Originality/value:This work applies for the first time to university websites some recently introduced techniques for automatic knowledge extraction based on web scraping,optical character recognition and nontrivial text mining operations(Bruni&Bianchi,2020).
文摘随着可获取遥感数据的增加和网络技术的飞速发展,遥感数据的在线自动一体化服务需求日益增长,而当前尚无主流的基于服务链的遥感数据在线可视化及自动化计算平台。该文基于B/S构架,应用工作流技术,提出了一种基于服务链的集数据查询服务、模型定制服务及信息发布服务于一体的遥感信息模型(remote sensing information models,RSIM)在线可视化定制和自动化计算的解决方案,使用户在友好的Web界面中可以一体化地按需完成从数据选择、模型设计到模型实现的动态过程。基于遥感数据各处理模块的可复用性,快速构建并实现了RSIM,是对遥感数据在线可视化定制和自动化服务的一次有益尝试。
