【24h】

Detecting geographic locations from web resources

机译:从Web资源检测地理位置

获取原文
获取原文并翻译 | 示例

摘要

The rapid pervasion of the web into users' daily lives has put much importance on capturing location-specific information on the web, due to the fact that most human activities occur locally around where a user is located. This is especially true in the increasingly popular mobile and local search environments. Thus, how to correctly and effectively detect geographic locations from web resources has become a key challenge to location-based web applications. In our previous work, we proposed to explicitly distinguish three types of locations for web resources, namely provider location, content location and serving location. Provider location is the physical location of the provider who owns the web resource; content location is the geographic location described in the web content; while serving location is the geographic scope that a web resource can reach. In this paper, we present a system that comprehensively employs a set of algorithms and different geographic sources by extracting geographic information from the web content, and mining hyperlink structures as well as user logs. As the result, only relevant geographic sources, rather than all of possible ones are used in computation of each category of web location. Finally, experimental results on large samples of web data show that our solution outperforms previous approaches.
机译:由于大多数人类活动都发生在用户所在的位置附近,因此,网络迅速渗透到用户的日常生活中对于在网络上捕获特定于位置的信息非常重要。在日益流行的移动和本地搜索环境中尤其如此。因此,如何正确和有效地从Web资源中检测地理位置已成为基于位置的Web应用程序的关键挑战。在我们以前的工作中,我们建议明确区分Web资源的三种类型的位置,即提供者位置,内容位置和服务位置。提供者位置是拥有Web资源的提供者的实际位置;内容位置是网络内容中描述的地理位置;服务位置是网络资源可以到达的地理范围。在本文中,我们提出了一种系统,该系统通过从Web内容中提取地理信息,挖掘超链接结构以及用户日志来全面采用一组算法和不同的地理资源。结果,在计算网站的每个类别时仅使用相关的地理资源,而不是所有可能的地理资源。最后,对大量Web数据样本的实验结果表明,我们的解决方案优于以前的方法。

著录项

  • 来源
  • 会议地点 Bremen(DE)
  • 作者单位

    Huazhong University of Science amp;

    Technology, Wuhan, P.R. China;

    Microsoft Research Asia, Beijing, P.R China;

    Microsoft Corporation;

    WEI-YING MA received the B.S. degree in electrical engineering from the national Tsing-Hua University in Taiwan in 1990, and the M.S. and Ph.D. degrees in electrical and computer engineering from the University of California at Santa Barbara (UCSB) in 1994 and 1997, respectively. From 1994 to 1997 he was engaged in the Alexandria Digital Library project in UCSB while completing his Ph.D. In June 1997, he joined the Hewlett-Packard Laboratories at Palo Alto, where he is currently a Staff Engineer in the lnternet Systems and Applications lab. His research interests include content-based image/video retrieval, image processing, computer vision. and neural networ;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 计算技术、计算机技术;
  • 关键词

    web location;

    机译:网站位置;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号