A Method of Deep Web Classification

Xu, Hexiang; Hao, Xin; Wang, Shuyun; Hu, Yunfa

doi:10.1109/icmlc.2007.4370847

Cited by 13 publications

(23 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…An example is a query interface for book search with text inputs as title, author, ISBN etc. the technique proposed by H. Xu et al in [11] deals with advance query interfaces for classification.…”

Section: Figure 2 a Simple Query Interfacementioning

confidence: 99%

“…All the deep web classification approaches proposed in [2], [7], [9], [11], [12], [13], [17], [18], [19] classify deep web sources under macro category domains except [14] which classifies deep web sources under micro category domains.…”

Section: A Macro Category Domain CDI Can Be Further Partitioned Into mentioning

confidence: 99%

“…In the recent years innovation to traditional supervised classification approach is introduced by the enhancement of data source features using some external knowledge sources. In the deep web classification technique [11], features are enhanced through building ontology model for each category domain.…”

Section: Organization Approachmentioning

confidence: 99%

“…The deep web classification approaches that choose query schema of the deep web source as a content representative are [2], [11], [14], [17], [18], [19]. In these approaches, the attribute labels along with their data types are extracted for classifying a data source in a domain.…”

Section: Visible Form Featuresmentioning

confidence: 99%

See 4 more Smart Citations

A Survey of Automatic Deep Web Classification Techniques

Noor¹,

Rashid²,

Rauf³

2011

IJCA

View full text Add to dashboard Cite

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.

show abstract

Section: Deep Web Classification Approachesmentioning

confidence: 99%

Section: Figure 2 a Simple Query Interfacementioning

confidence: 99%

Section: A Macro Category Domain CDI Can Be Further Partitioned Into mentioning

confidence: 99%

Section: Organization Approachmentioning

confidence: 99%

Section: Visible Form Featuresmentioning

confidence: 99%

See 3 more Smart Citations

A Survey of Automatic Deep Web Classification Techniques

Noor¹,

Rashid²,

Rauf³

2011

IJCA

View full text Add to dashboard Cite

show abstract

Deep Web Query Interface Understanding and Integration

Dragut

Meng

2012

Synthesis Lectures on Data Management

View full text Add to dashboard Cite

Multimodal Classification of Onion Services for Proactive Cyber Threat Intelligence Using Explainable Deep Learning

et al. 2022

View full text Add to dashboard Cite

The dark web has been confronted with a significant increase in the number and variety of onion services of illegitimate and criminal intent. Anonymity, encryption, and the technical complexity of the Tor network are key challenges in detecting, disabling, and regulating such services. Instead of tracking an operational location, cyber threat intelligence can become more proactive by utilizing recent advances in Artificial Intelligence (AI) to detect and classify onion services based on the content, as well as provide an interpretation of the classification outcome. In this paper, we propose a novel multimodal classification approach based on explainable deep learning that classifies onion services based on the image and text content of each site. A Convolutional Neural Network with Gradient-weighted Class Activation Mapping (Grad-CAM) and a pre-trained word embedding with Bahdanau additive attention are the core capabilities of this approach that classify and contextualize the representative features of an onion service. We demonstrate the superior classification accuracy of this approach as well as the role of explainability in decision-making that collectively enables proactive cyber threat intelligence in the dark web.

show abstract

A Method of Deep Web Classification

Cited by 13 publications

References 12 publications

A Survey of Automatic Deep Web Classification Techniques

A Survey of Automatic Deep Web Classification Techniques

Deep Web Query Interface Understanding and Integration

Multimodal Classification of Onion Services for Proactive Cyber Threat Intelligence Using Explainable Deep Learning

Contact Info

Product

Resources

About