Subject-Oriented Classification Based on Scale Probing in the Deep Web

Nie, Tiezheng; Shen, Derong; Yu, Ge; Kou, Yue

doi:10.1109/waim.2008.85

Cited by 5 publications

(14 citation statements)

References 11 publications

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“…Although there are some exceptional overlapping zones, but generally we can classify the techniques encountering structured web sources as having advanced query forms unless specified explicitly as in [12]. Thus the technique proposed in [2], [13], [14], [17], [18], [19] works with advanced query forms.…”

Section: R Denotes the Results Page Retrieved In Response To User's Qumentioning

confidence: 99%

“…Several deep web classification approaches are proposed in the literature [2], [7], [9], [11], [12], [13], [14], [17], [18], [19]. Here we provide a comprehensive comparative analysis of the above approaches regarding their organization approach, content representative extraction methodology, granularity of category domains, classification methodology and performance evaluation metrics.…”

Section: Deep Web Classification Approachesmentioning

confidence: 99%

“…The rest of classification is performed on the bases of acquired knowledge. Here in this study the techniques proposed in [9], [12], [13], [14], [18] classify deep web sources using supervised classification approach. In the recent years innovation to traditional supervised classification approach is introduced by the enhancement of data source features using some external knowledge sources.…”

Section: Organization Approachmentioning

confidence: 99%

“…Query Probing is a very common method adopted by most of the supervised deep web classification approaches [9], [12], [13], [14]. In query probing, queries are designed through special techniques and are probed against the deep web databases.…”

Section: Query Probingmentioning

confidence: 99%

“…The sample instances are gathered from the static web pages and query result pages. The data scale of the query result is the content representative of the data source and is employed to determine its category [14].…”

Section: Query Probingmentioning

confidence: 99%

See 4 more Smart Citations

A Survey of Automatic Deep Web Classification Techniques

Noor¹,

Rashid²,

Rauf³

2011

IJCA

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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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