Processing online news streams for large-scale semantic analysis

Milos Krstajic, Florian Mansmann, A. Stoffel, M. Atkinson, D. Keim
{"title":"Processing online news streams for large-scale semantic analysis","authors":"Milos Krstajic, Florian Mansmann, A. Stoffel, M. Atkinson, D. Keim","doi":"10.1109/ICDEW.2010.5452710","DOIUrl":null,"url":null,"abstract":"While Internet has enabled us to access a vast amount of online news articles originating from thousands of different sources, the human capability to read all these articles has stayed rather constant. Usually, the publishing industry takes over the role of filtering this enormous amount of information and presenting it in an appropriate way to the group of their subscribers. In this paper, the semantic analysis of such news streams is discussed by introducing a system that streams online news collected by the Europe Media Monitor to our proposed semantic news analysis system. Thereby, we describe in detail the emerging challenges and the corresponding engineering solutions to process incoming articles close to real-time. To demonstrate the use of our system, the case studies show a) temporal analysis of entities, such as institutions or persons, and b) their co-occurence in news articles.","PeriodicalId":442345,"journal":{"name":"2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)","volume":"163 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"34","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDEW.2010.5452710","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 34

Abstract

While Internet has enabled us to access a vast amount of online news articles originating from thousands of different sources, the human capability to read all these articles has stayed rather constant. Usually, the publishing industry takes over the role of filtering this enormous amount of information and presenting it in an appropriate way to the group of their subscribers. In this paper, the semantic analysis of such news streams is discussed by introducing a system that streams online news collected by the Europe Media Monitor to our proposed semantic news analysis system. Thereby, we describe in detail the emerging challenges and the corresponding engineering solutions to process incoming articles close to real-time. To demonstrate the use of our system, the case studies show a) temporal analysis of entities, such as institutions or persons, and b) their co-occurence in news articles.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
处理在线新闻流进行大规模语义分析
虽然互联网使我们能够访问来自成千上万不同来源的大量在线新闻文章,但人类阅读所有这些文章的能力却保持不变。通常,出版业负责过滤海量信息,并以适当的方式呈现给他们的订阅者群体。本文通过介绍一个将欧洲媒体监测网站收集的在线新闻流到我们提出的语义新闻分析系统的系统,来讨论这些新闻流的语义分析。因此,我们详细描述了新出现的挑战和相应的工程解决方案,以接近实时地处理传入的文章。为了演示我们的系统的使用,案例研究展示了a)实体的时间分析,例如机构或个人,以及b)它们在新闻文章中的共同出现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Fast algorithms for time series mining Ontology alignment argumentation with mutual dependency between arguments and mappings A first step towards integration independence Towards enterprise software as a service in the cloud U-DBSCAN : A density-based clustering algorithm for uncertain objects
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1