Abstract
The inaccessible nature of the Deep Web motivated us to propose a learning-based application framework for exploring Deep Web, based on the query provided by the user, through TOR interface. The proposed framework, DWIRS: Deep Web Indexing and Ranking Search, employs two algorithms: BSVM (Boosted Support Vector Machine) for indexing, and, BRF (Boosted Random Forest) for ranking Deep Web pages. To evaluate the performance of DWIRS framework, DWIRS Document Clustering Workbench, a standalone GUI application has been developed and validated. A mobile application for exploring Deep Web has also been developed. The purposed DWIRS framework is efficient.
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