Web pages - especially dynamically generated ones - contain several items that cannot be classified as the "primary content," e.g., navigation sidebars, advertisements, copyright notices, etc. Most clients and end-users search for the primary content, and largely do not seek the noninformative content. A tool that assists an end-user or application to search and process information from Web pages automatically, must separate the "primary content sections" from the other content sections. We call these sections as "Web page blocks" or just "blocks." First, a tool must segment the Web pages into Web page blocks and, second, the tool must separate the primary content blocks from the noninformative content blocks. In this paper, we formally define Web page blocks and devise a new algorithm to partition an HTML page into constituent Web page blocks. We then propose four new algorithms, ContentExtractor, FeatureExtractor, K-FeatureExtractor, and L-Extractor. These algorithms identify primary content blocks by 1) looking for blocks that do not occur a large number of times across Web pages, by 2) looking for blocks with desired features, and by 3) using classifiers, trained with block-features, respectively. While operating on several thousand Web pages obtained from various Web sites, our algorithms outperform several existing algorithms with respect to runtime and/or accuracy. Furthermore, we show that a Web cache system that applies our algorithms to remove noninformative content blocks and to identify similar blocks across Web pages can achieve significant storage savings.

Niche Search Engines offer an efficient alternative to traditional search engines when the results returned by general-purpose search engines do not provide a sufficient degree of relevance. By taking advantage of their domain of concentration they achieve higher relevance and offer enhanced features. We discuss a new niche search engine, eBizSearch, based on the technology of CiteSeer and dedicated to e-business and e-business documents. We present the integration of CiteSeer in the framework of eBizSearch and the process necessary to tune the whole system towards the specific area of e-business. We also discuss how using machine learning algorithms we generate metadata to make eBizSearch Open Archives compliant. eBizSearch is a publicly available service and can be reached at [3].