An Efficient Image Based Approach for Extraction of Deep Web Data

The Internet presents a huge amount of useful information which is usually formatted for its users, which makes it difficult to extract relevant data from various sources. Deep Web contents are extracted by submitting the queries to semi structured Web databases and the returned data records are enwrapped in dynamically generated Web pages. Extracting structured data from deep Web pages is a challenging problem due to the underlying intricate semi structures of such pages. Until now, a large number of techniques have been proposed to address this problem, but all of them have inherent limitations because they are Web-page-programming-language dependent. As the popular two-dimensional media, the contents on Web pages are always displayed regularly for users to browse. This motivates us to seek a different way for deep Web data extraction to overcome the limitations of previous works by utilizing some interesting common visual features on the deep Web pages. In this paper, an image based approach that is Webpage programminglanguage-independent is proposed using XML like structure and the help of VIPS algorithm. This approach primarily focuses the visual features on the hidden Web pages to implement deep Web data extraction, including data record extraction and data item extraction. We also propose a new evaluation measure revision to capture the amount of human effort needed to produce perfect extraction. Our experiments on a large set of Web databases show that the proposed vision-based approach is highly effective for deep Web data extraction.