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Reseach Article

A Survey of Automatic Deep Web Classification Techniques

by Umara Noor, Zahid Rashid, Azhar Rauf
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 19 - Number 6
Year of Publication: 2011
Authors: Umara Noor, Zahid Rashid, Azhar Rauf

Umara Noor, Zahid Rashid, Azhar Rauf . A Survey of Automatic Deep Web Classification Techniques. International Journal of Computer Applications. 19, 6 ( April 2011), 43-50. DOI=10.5120/2362-3099

@article{ 10.5120/2362-3099,
author = { Umara Noor, Zahid Rashid, Azhar Rauf },
title = { A Survey of Automatic Deep Web Classification Techniques },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 19 },
number = { 6 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 43-50 },
numpages = {9},
url = { },
doi = { 10.5120/2362-3099 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:06:19.162314+05:30
%A Umara Noor
%A Zahid Rashid
%A Azhar Rauf
%T A Survey of Automatic Deep Web Classification Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 19
%N 6
%P 43-50
%D 2011
%I Foundation of Computer Science (FCS), NY, USA

To devise vision of the next generation of the web, deep web technologies have gained larger attention in a last few years. An eminent feature of next generation of web is the automation of tasks. A large part of Deep web comprises of online structured domain specific databases that are accessed using web query interfaces. The information contained in these databases is related to a particular domain. This highly relevant information is more suitable for satisfying the information needs of the users and large scale deep web integration. In order to make this extraction and integration process easier, it is necessary to classify the deep web databases into standard\ non-standard category domains. There are mainly two types of classification techniques i.e. manual and automatic. As the size of deep web is increasing at an exponential rate with the passage of time, it has become nearly impossible to classify these deep web search sources manually into their respective domains. For this purpose, several automatic deep web classification techniques have been proposed in the literature. In this paper apart from the literature survey, we propose a framework for analysis of automatic classification techniques of deep web. The framework provides a baseline for the analysis of rudiments of automatic classification techniques based on the parameters such as structured, unstructured, simple/advance query forms, content representative extraction methodology, level of classification, performance evaluation criteria and its results. Furthermore, we studied a number of automatic deep web classification techniques in the light of proposed framework.

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Index Terms

Computer Science
Information Sciences


Deep web web databases data integration domain concepts Survey