QA-Pagelet: Data preparation techniques for large-scale data analysis of the Deep Web Conference Paper

abstract

• This paper presents the QA-Pagelet as a fundamental data preparation technique for large-scale data analysis of the Deep Web. To support QA-Pagelet extraction, we present the Thor framework for sampling, locating, and partioning the QA-Pagelets from the Deep Web. Two unique features of the Thor framework are 1) the novel page clustering for grouping pages from a Deep Web source into distinct clusters of control-flow dependent pages and 2) the novel subtree filtering algorithm that exploits the structural and content similarity at subtree level to identify the QA-Pagelets within highly ranked page clusters. We evaluate the effectiveness of the Thor framework through experiments using both simulation and real data sets. We show that Thor performs well over millions of Deep Web pages and over a wide range of sources, including e-Commerce sites, general and specialized search engines, corporate Web sites, medical and legal resources, and several others. Our experiments also show that the proposed page clustering algorithm achieves low-entropy clusters, and the subtree filtering algorithm identifies QA-Pagelets with excellent precision and recall. 2005 IEEE.

authors

• Caverlee, James

published proceedings

• IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

author list (cited authors)

• Caverlee, J., & Liu, L.

citation count

• 8

complete list of authors

• Caverlee, J||Liu, L

publication date

• September 2005

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Transactions on Knowledge and Data Engineering  Journal

keywords

• Clustering
• Data Extraction
• Data Preparation
• Deep Web
• Pagelets

Digital Object Identifier (DOI)

• 10.1109/TKDE.2005.151

start page

• 1247

end page

• 1262

volume

• 17

issue

• 9

URL

• http://dx.doi.org/10.1109/tkde.2005.151