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    题名: FiVaTech: Page-Level Web Data Extraction from Template Pages
    作者: Kayed,M;Chang,CH
    贡献者: 資訊工程學系
    日期: 2010
    上传时间: 2012-03-27 18:55:47 (UTC+8)
    出版者: 國立中央大學
    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.
    關聯: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
    显示于类别:[資訊工程學系] 期刊論文

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