Deep Web Data Source Classification Based on Query Interface Context

Author

Cui, Zilu ; Fu, Yuchen

Author_Institution

Sch. of Comput. Sci. & Technol., Soochow Univ., Suzhou, China

fYear

2012

fDate

17-19 Aug. 2012

Firstpage

329

Lastpage

332

Abstract

As the volume of information in the Deep Web grows, a Deep Web data source classification algorithm based on query interface context is presented. Two methods are combined to get the search interface similarity. One is based on the vector space. The classical TF-IDF statistics are used to gain the similarity between search interfaces. The other is to compute the two pages semantic similarity by the use of HowNet. Based on the K-NN algorithm, a WDB classification algorithm is presented. Experimental results show this algorithm generates high-quality clusters, measured both in terms of entropy and F-measure. It indicates the practical value of application.

Keywords

Internet; pattern classification; pattern matching; query processing; Deep Web data source classification; F-measure; HowNet; K-NN algorithm; WDB classification algorithm; classical TF-IDF statistics; high-quality clusters; pages semantic similarity; query interface context; search interface similarity; vector space; Catalogs; Classification algorithms; Databases; Entropy; Information systems; Semantics; Web pages; Deep Web; HowNet; K-NN algorithm; data source classification; semantic classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational and Information Sciences (ICCIS), 2012 Fourth International Conference on

Conference_Location

Chongqing

Print_ISBN

978-1-4673-2406-9

Type

conf

DOI

10.1109/ICCIS.2012.117

Filename

6300503