A Survey of Automatic Deep Web Classification Techniques

Abstract

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.

Deep Web Query Interface Understanding and Integration

“…A number of query interface clustering algorithms have been developed [Noor et al, 2011]. They can be distinguished by the type of data they employ for clustering and by their underlying clustering approach.…”

Todweb

“…Such similarity matching is also called as semantic mapping. Semantic mapping between query schemas is not an easy job as discussed in the context of data integration in [24].…”

Enhancing the Performance of Proxy Cache Management through Browsing Behavior-based Learning Mechanism

“…Generally, users search for their desired information by looking at the default groups or directories that appeared on the search engine screen, such as entertainment, sports, computers, books, etc. The advantage of these directories is the development of a high quality directory service; however, the approach cannot be applied to this research due to the limitation of scalability [3].…”