DCADE: divide and conquer alignment with dynamic encoding for full page data extraction

Oviliani Yenty Yuliana, Chia Hui Chang

研究成果: 雜誌貢獻期刊論文同行評審

4 引文 斯高帕斯(Scopus)


In this paper, we consider the problem of full schema induction from either multiple list pages or singleton pages with the same template. Existing approaches do not work well for this problem because they use fixed abstraction schemes that are suitable for data-rich detection, but they are not appropriate for small records and complex data found in other sections. We propose an unsupervised full schema web data extraction via Divide-and-Conquer Alignment with Dynamic Encoding (DCADE for short). We define the Content Equivalence Class (CEC) and Typeset Equivalence Class (TEC) based on leaf node content. We then combine HTML attributes (i.e., id and class) in the paths for various levels of encoding, so that the proposed algorithm can align leaf nodes by exploring patterns at various levels from specific to general. We conducted experiments on 49 real-world websites used in TEX and ExAlg. The proposed DCADE achieved a 0.962 F1 measure for non-recordset data extraction (denoted by FD), and a 0.936 F1 measure for recordset data extraction (denoted by FS), which outperformed other page-level web data extraction methods, i.e., DCA (FD= 0.660), TEX (FD= 0.454 and FS= 0.549), RoadRunner (FD= 0.396 and FS= 0.330), and UWIDE (FD= 0.260 and FS= 0.081).

頁(從 - 到)271-295
期刊Applied Intelligence
出版狀態已出版 - 1 2月 2020


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