Using Clustering for Web Information Extraction
نویسندگان
چکیده
This paper introduces an approach that achieves automated data extraction for semi-structured Web pages by using clustering to group text tokens and data tuples into clusters. This approach uses both HTML and text features of text tokens to detect the similarities between them. After clustering, similar text tokens are expected to be in the same text clusters and labeled with the same text cluster IDs. Clustering is also applied on data tuples to group them into tuple clusters. Basically, a tuple cluster is a strong candidate of a repetitive data region. The similarities between data tuples are computed by applying Smith-Waterman algorithm on sequences of text cluster IDs of the text tokens that data tuples contain.
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