Probe, cluster, and discover: Focused extraction of QA-Pagelets from the Deep Web

被引：12

作者：

Caverlee, J ^{[1
]}

Liu, L ^{[1
]}

Buttler, D ^{[1
]}

机构：

[1] Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA

来源：

20TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING, PROCEEDINGS | 2004年

关键词：

D O I：

10.1109/ICDE.2004.1319988

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper we introduce the concept of a QA-Pagelet to refer to the content region in a dynamic page that contains query matches. We present THOR, a scalable and efficient mining system for discovering and extracting QA-Pagelets from the Deep Web. A unique feature of THOR is its two-phase extraction framework. In the first phase, pages from a deep web site are grouped into distinct clusters of structurally-similar pages. In the second phase,pages from each page cluster are examined through a subtree filtering algorithm that exploits the structural and content similarity at subtree level to identify the QA-Pagelets.

引用

页码：103 / 114

页数：12