FiVaTech: Page-level web data extraction from template pages

Mohammed Kayed, Chia Hui Chang

研究成果: 雜誌貢獻期刊論文同行評審

105 引文 斯高帕斯(Scopus)

摘要

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.

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文章編號4815243
頁(從 - 到)249-263
頁數15
期刊IEEE Transactions on Knowledge and Data Engineering
22
發行號2
DOIs
出版狀態已出版 - 2月 2010

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