Using clustering for web information extraction

被引：0
|
作者：
Phong, Le [1 ]
Vuong, Bao [1 ]
Gao, Xiaoying [1 ]
机构：
[1] Victoria Univ Wellington, Sch Math Stat & Comp Sci, POB 600, Wellington, New Zealand
来源：
AI 2007: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2007年 / 4830卷
关键词：
information extraction; clustering; Smith-Waterman algorithm;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper introduces an approach that achieves automated data extraction from semi-structured Web pages by clustering. Both HTML tags and the textual features of text tokens are considered for similarity comparison. The first clustering process groups similar text tokens into the same text clusters, and the second clustering process groups similar data tuples into tuple clusters. A tuple cluster is a strong candidate of a repetitive data region.
引用
收藏
页码：415 / +
页数：2
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