FiVaTech: Page-level web data extraction from template pages

Mohammed Kayed, Chia Hui Chang

Research output: Contribution to journalArticlepeer-review

99 Scopus citations

Abstract

Web data extraction has been an important part for many Web data analysis applications. In this paper, we formulate the data extraction problem as the decoding process of page generation based on structured data and tree templates. We propose an unsupervised, page-level data extraction approach to deduce the schema and templates for each individual Deep Website, which contains either singleton or multiple data records in one Webpage. FiVaTech applies tree matching, tree alignment, and mining techniques to achieve the challenging task. In experiments, FiVaTech has much higher precision than EXALG and is comparable with other record-level extraction systems like ViPER and MSE. The experiments show an encouraging result for the test pages used in many state-of-the-art Web data extraction works.

Original languageEnglish
Article number4815243
Pages (from-to)249-263
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Volume22
Issue number2
DOIs
StatePublished - Feb 2010

Keywords

  • Multiple trees merging
  • Semistructured data
  • Web data extraction
  • Wrapper induction

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