The World-Wide Web continues to grow rapidly, which makes exploiting all available information a challenge. Search engines such as Google index an unprecedented amount of information, but still do not provide access to valuable content in text databases "hidden" behind search interfaces. For example, current search engines largely ignore the contents of the Library of Congress, the US Patent and Trademark database, newspaper archives, and many other valuable sources of information because their contents are not "crawlable." However, users should be able to find the information that they need with as little effort as possible, regardless of whether this information is crawlable or not. As a significant step towards this goal, we have designed algorithms that support browsing and searching---the two dominant ways of finding information on the web---over "hidden-web" text databases.; To support browsing, we have developed QProber, a system that automatically categorizes hidden-web text databases in a classification scheme, according to their topical focus. QProber categorizes databases without retrieving any document. Instead, QProber uses just the number of matches generated from a small number of topically focused query probes. The query probes are automatically generated using state-of-the-art supervised machine learning techniques and are typically short. QProber's classification approach is sometimes orders of magnitude faster than approaches that require document retrieval.; To support searching, we have developed crucial building blocks for constructing sophisticated metasearchers, which search over many text databases at once through a unified query interface. For scalability and effectiveness, it is crucial for a metasearcher to have a good database selection component and send queries only to databases with relevant content. Usually, database selection algorithms rely on statistics that characterize the contents of each database. Unfortunately, many hidden-web text databases are completely autonomous and do not report any summaries of their contents. To build content summaries for such databases, we extract a small, topically focused document sample from each database during categorization and use it to build the respective content summaries. A potential problem with content summaries derived from document samples is that any reasonably small sample will suffer from data sparseness and will riot contain many words that appear in the database. (Abstract shortened by UMI.)
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