By Weiyi Meng, Clement Yu, M. Tamer Ozsu
One of the seek instruments presently on the net, se's are the main popular due to the recognition of significant se's similar to Google and Yahoo!. whereas tremendous profitable, those significant se's do have severe barriers. This e-book introduces large-scale metasearch engine know-how, which has the aptitude to beat the restrictions of the search engines. primarily, a metasearch engine is a seek method that helps unified entry to a number of current se's via passing the queries it gets to its part se's and aggregating the again effects right into a unmarried ranked checklist. A large-scale metasearch engine has hundreds of thousands or extra part se's. whereas metasearch engines have been in the beginning encouraged through their skill to mix the hunt assurance of a number of se's, there also are different advantages similar to the aptitude to acquire greater and more energizing effects and to arrive the Deep internet. the next significant elements of large-scale metasearch engines could be mentioned intimately during this e-book: seek engine choice, seek engine incorporation, and end result merging. hugely scalable and automatic recommendations for those elements are emphasised. The authors make a powerful case for the viability of the large-scale metasearch engine know-how as a aggressive expertise for internet seek. desk of Contents: creation / Metasearch Engine structure / seek Engine choice / seek Engine Incorporation / end result Merging / precis and destiny learn
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Extra resources for Advanced Metasearch Engine Technology
Major search engines rely on their crawlers to collect documents from numerous Web servers. However, these crawlers cannot keep up with the fast changing Web contents due to the huge numbers of Web pages and Web servers involved, as well as the constantly changing nature of the Web. It typically takes from several days to couple of weeks for newly updated or added contents to be crawled or re-crawled. Consequently, the contents indexed by major search engines are usually several days out-of-date on average.
There are two approaches to building each type of metasearch engines: • Major search engine approach. This approach uses a small number of popular major search engines to build a metasearch engine. Thus, to build a general-purpose metasearch engine 22 2. , Bing (MSN) and Ask. Similarly, to build a special-purpose metasearch engine for a given domain, we can use a small number of major search engines in that domain. For example, for the news domain, Google News, Yahoo! , can be used. • Large-scale metasearch engine approach.
While most rough search engine representatives are generated with human involvement, automatically generated rough representatives exist. In Q-Pilot (Sugiura and Etzioni, 2000), each search engine is represented by a vector of terms with weights. The terms are obtained from the interface page of the search engine or from the pages that have links to the search engine. In the latter case, only terms that appear in the same line as the link to the search engine are used, and the weight of each term is the number of back link documents that contain the term.