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An Efficient Annotation of Search Results Based on Feature

A. Jebha, R. Tamilselvi



With the increased number of web databases, major part of deep web is one of the bases of database. In several search engines, encoded data in the returned resultant pages from the web often comes from structured databases which are referred as Web databases (WDB). A result page returned from WDB has multiple search records (SRR).Data units obtained from these databases are encoded into the dynamic resultant pages for manual processing. In order to make these units to be machine process able, relevant information are extracted and labels of data are assigned meaningfully. In this paper, feature ranking is proposed to extract the relevant information of extracted feature from WDB. Feature ranking is practical to enhance ideas of data and identify relevant features. This research explores the performance of feature ranking process by using the linear support vector machines with various feature of WDB database for annotation of relevant results. Experimental result of proposed system provides better result when compared with the earlier methods.

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Performance graph

Annotation without ranking

Annotation with feature ranking

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