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  • 标题:Unsupervised Structured Data Extraction from Template-generated Web Pages
  • 本地全文:下载
  • 作者:Tomas Grigalis ; Antanas Čenys
  • 期刊名称:Journal of Universal Computer Science
  • 印刷版ISSN:0948-6968
  • 出版年度:2014
  • 卷号:20
  • 期号:2
  • 页码:169-192
  • 出版社:Graz University of Technology and Know-Center
  • 摘要:This paper studies structured data extraction from template-generated Web pages. Such pages contain most of structured data on the Web. Extracted structured data can be later integrated and reused in very big range of applications, such as price comparison portals, business intelligence tools, various mashups and etc. It encourages industry and academics to seek automatic solutions. To tackle the problem of automatic structured Web data extraction we present a new approach - structured data extraction based on clustering visually similar Web page elements. Our method called ClustVX combines visual and pure HTML features of Web page to cluster visually similar Web page elements and then extract structured Web data. ClustVX can extract structured data from Web pages where more than one data record is present. With extensive experimental evaluation on three benchmark datasets we demonstrate that ClustVX achieves better results than other state-of-the-art automatic structured Web data extraction methods.
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