Probe, cluster, and discover: focused extraction of QA-Pagelets from the deep Web

Author

Caverlee, James ; Liu, Ling ; Buttler, David

Author_Institution

Coll. of Comput., Georgia Inst. of Technol., Atlanta, GA, USA

fYear

2004

fDate

30 March-2 April 2004

Firstpage

103

Lastpage

114

Abstract

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.

Keywords

Internet; Web sites; content management; data mining; feature extraction; online front-ends; pattern clustering; query formulation; QA-Pagelet discovery; QA-Pagelet extraction; content region; deep Web; dynamic page; page cluster; query matches; subtree filtering algorithm; two-phase extraction framework; Data mining; Databases; Educational institutions; Filtering algorithms; Indexing; Navigation; Probes; Robustness; Search engines; Web pages;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Engineering, 2004. Proceedings. 20th International Conference on

ISSN

1063-6382

Print_ISBN

0-7695-2065-0

Type

conf

DOI

10.1109/ICDE.2004.1319988

Filename

1319988