Vertical Meta Search Engine with Query Expansion through ARFT: A Vertical Meta Search Engine with QueryExpansion through Artificial RelevanceFeedback Technique

Kushwaha, Satpal Singh; Hemrajani, Prashant; Joshi, Sandeep

ISBN 10: 6202922400 / ISBN 13: 9786202922401

Editore: LAP LAMBERT Academic Publishing, 2020

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Dati bibliografici

• Titolo: Vertical Meta Search Engine with Query Expansion through ARFT: A Vertical Meta Search Engine with QueryExpansion through Artificial RelevanceFeedback Technique
• Editore: LAP LAMBERT Academic Publishing
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 6202922400
• ISBN 13: 9786202922401
• Rilegatura: Brossura
• Condizione: New

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Riassunto

World Wide Web is growing day by day so with this rapid development in internet size,Information extraction on Internet is gaining its importance day by day. Nowadays there aremillions of Websites and billions of homepages available on the Internet. A large amount of onlineinformation resides on the invisible web – web pages generated dynamically from databasesand other data sources hidden from the user. They are not indexed by a static URL but isgenerated when queries are asked via a search engine (we denote them as specialized searchengines or vertical search engine). OpenFind states that it indexes 3.5 billion Web pages; Googleclaims 2.4 billion, AlltheWeb - 2.1 billion, Inktomi - a little more than 2 billion, WiseNut - 1.5billion and AltaVista - 1 billion Web pages. No search engine index more than one third of thetotal size of the web. So from this big collection of web pages information retrieval is a verycrucial task. The user query plays a vital role in the information retrieval process.