Wikinformetrics:构建和描述用于信息计量目的的开放维基百科知识图谱数据集

IF 4.1 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE
Wenceslao Arroyo-Machado, D. Torres-Salinas, R. Costas
{"title":"Wikinformetrics:构建和描述用于信息计量目的的开放维基百科知识图谱数据集","authors":"Wenceslao Arroyo-Machado, D. Torres-Salinas, R. Costas","doi":"10.1162/qss_a_00226","DOIUrl":null,"url":null,"abstract":"Abstract Wikipedia is one of the most visited websites in the world and is also a frequent subject of scientific research. However, the analytical possibilities of Wikipedia information have not yet been analyzed considering at the same time both a large volume of pages and attributes. The main objective of this work is to offer a methodological framework and an open knowledge graph for the informetric large-scale study of Wikipedia. Features of Wikipedia pages are compared with those of scientific publications to highlight the (dis)similarities between the two types of documents. Based on this comparison, different analytical possibilities that Wikipedia and its various data sources offer are explored, ultimately offering a set of metrics meant to study Wikipedia from different analytical dimensions. In parallel, a complete dedicated data set of the English Wikipedia was built (and shared) following a relational model. Finally, a descriptive case study is carried out on the English Wikipedia data set to illustrate the analytical potential of the knowledge graph and its metrics.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":"3 1","pages":"931-952"},"PeriodicalIF":4.1000,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Wikinformetrics: Construction and description of an open Wikipedia knowledge graph data set for informetric purposes\",\"authors\":\"Wenceslao Arroyo-Machado, D. Torres-Salinas, R. Costas\",\"doi\":\"10.1162/qss_a_00226\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Wikipedia is one of the most visited websites in the world and is also a frequent subject of scientific research. However, the analytical possibilities of Wikipedia information have not yet been analyzed considering at the same time both a large volume of pages and attributes. The main objective of this work is to offer a methodological framework and an open knowledge graph for the informetric large-scale study of Wikipedia. Features of Wikipedia pages are compared with those of scientific publications to highlight the (dis)similarities between the two types of documents. Based on this comparison, different analytical possibilities that Wikipedia and its various data sources offer are explored, ultimately offering a set of metrics meant to study Wikipedia from different analytical dimensions. In parallel, a complete dedicated data set of the English Wikipedia was built (and shared) following a relational model. Finally, a descriptive case study is carried out on the English Wikipedia data set to illustrate the analytical potential of the knowledge graph and its metrics.\",\"PeriodicalId\":34021,\"journal\":{\"name\":\"Quantitative Science Studies\",\"volume\":\"3 1\",\"pages\":\"931-952\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2022-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Quantitative Science Studies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1162/qss_a_00226\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"INFORMATION SCIENCE & LIBRARY SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quantitative Science Studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1162/qss_a_00226","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 4

摘要

维基百科是世界上访问量最大的网站之一,也是科学研究的频繁对象。然而,同时考虑到大量的页面和属性,维基百科信息的分析可能性尚未得到分析。这项工作的主要目的是为维基百科的信息大规模研究提供一个方法论框架和一个开放的知识图谱。将维基百科页面的特征与科学出版物的特征进行比较,以突出两类文档之间的(不)相似之处。基于这种比较,我们探索了维基百科及其各种数据源提供的不同分析可能性,最终提供了一组旨在从不同分析维度研究维基百科的指标。与此同时,一个完整的英文维基百科专用数据集也按照关系模型建立(并共享)。最后,对英文维基百科数据集进行了描述性案例研究,以说明知识图及其度量的分析潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wikinformetrics: Construction and description of an open Wikipedia knowledge graph data set for informetric purposes
Abstract Wikipedia is one of the most visited websites in the world and is also a frequent subject of scientific research. However, the analytical possibilities of Wikipedia information have not yet been analyzed considering at the same time both a large volume of pages and attributes. The main objective of this work is to offer a methodological framework and an open knowledge graph for the informetric large-scale study of Wikipedia. Features of Wikipedia pages are compared with those of scientific publications to highlight the (dis)similarities between the two types of documents. Based on this comparison, different analytical possibilities that Wikipedia and its various data sources offer are explored, ultimately offering a set of metrics meant to study Wikipedia from different analytical dimensions. In parallel, a complete dedicated data set of the English Wikipedia was built (and shared) following a relational model. Finally, a descriptive case study is carried out on the English Wikipedia data set to illustrate the analytical potential of the knowledge graph and its metrics.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Quantitative Science Studies
Quantitative Science Studies INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
12.10
自引率
12.50%
发文量
46
审稿时长
22 weeks
期刊介绍:
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信