The deep Web (or Deepnet, invisible Web or hidden Web) refers to World Wide Web content that is not part of the surface Web indexed by search engines. It is estimated that the deep Web is several magnitudes larger than the surface Web (Bergman, 2001). Dr. Jill Ellsworth coined the term "Invisible Web" in 1994 to refer to websites that are not registered with any search engine (Bergman, 2001).
Less commonly, the term deep Web may represent deeper interaction.

Size
Deep Web resources may be classified into one or more of the following categories:

Dynamic content - dynamic pages which are returned in response to a submitted query or accessed only through a form (especially if open-domain input elements e.g. text fields are used; such fields are hard to navigate without domain knowledge).
Unlinked content - pages which are not linked to by other pages, which may prevent Web crawling programs from accessing the content. This content is referred to as pages without backlinks (or inlinks).
Private Web - sites that require registration and login (password-protected resources).
Contextual Web - pages with content varying for different access contexts (e.g. ranges of client IP addresses or previous navigation sequence).
Limited access content - sites that limit access to their pages in a technical way (e.g., using the Robots Exclusion Standard, CAPTCHAs or pragma:no-cache/cache-control:no-cache HTTP headers), prohibiting search engines from browsing them and creating cached copies.
Scripted content - pages that are only accessible through links produced by JavaScript as well as content dynamically downloaded from Web servers via Flash or AJAX solutions.
Non-HTML/text content - textual content encoded in multimedia (image or video) files or specific file formats not handled by search engines. Deep resources
To discover content on the Web, search engines use web crawlers that follow hyperlinks. This technique is ideal for discovering resources on the surface Web but is often ineffective at finding deep Web resources. For example, these crawlers do not attempt to find dynamic pages that are the result of database queries due to the infinite number of queries that are possible. It has been noted that this can be (partially) overcome by providing links to query results, but this could unintentionally inflate the popularity (e.g., PageRank) for a member of the deep Web.
In 2005, Yahoo! made a small part of the deep web searchable by releasing Yahoo! Subscriptions. This search engine searches through a few subscription-only web sites.
Some search tools such as Pipl are being designed to retrieve information from the deep Web; their crawlers are set to identify and somehow interact with searchable databases, aiming to provide access to deep Web content.

Accessing
Researchers have been exploring how the deep Web can be crawled in an automatic fashion. Raghavan and Garcia-Molina (2001) presented an architectural model for a hidden-Web crawler that used key terms provided by users or collected from the query interfaces to query a Web form and crawl the deep Web resources. Ntoulas et al. (2005) created a hidden-Web crawler that automatically generated meaningful queries to issue against search forms. Their crawler generated promising results, but the problem is far from being solved.
Since a large amount of useful data and information resides in the deep Web, search engines have begun exploring alternative methods to crawl the deep Web. Google's Sitemap Protocol and mod oai are mechanisms that allow search engines and other interested parties to discover deep Web resources on particular Web servers. Both mechanisms allow Web servers to advertise the URLs that are accessible on them, thereby allowing automatic discovery of resources that are not directly linked to the surface Web.
Another way to access the deep Web is to crawl it by subject category or vertical. Since traditional engines have difficulty crawling and indexing deep Web pages and their content, deep Web search engines like CloserLookSearch, and Northern Light create specialty engines by topic to search the deep Web. Because these engines are narrow in their data focus, they are built to access specified deep Web content by topic. These engines can search dynamic or password protected databases that are otherwise closed to search engines.

History

Personal Library Software (Dec 1996) press release announcing @1 as an "Invisible Web" search service
Panagiotis Ipeirotis, Luis Gravano, and Mehran Sahami (2001). "Probe, Count, and Classify: Categorizing Hidden-Web Databases". In Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data: 67-78. 
Gary Price & Chris Sherman (July 2001). The Invisible Web : Uncovering Information Sources Search Engines Can't See. CyberAge Books, ISBN 0-910965-51-X.
Michael K. Bergman (Aug 2001). "The Deep Web: Surfacing Hidden Value". The Journal of Electronic Publishing 7 (1). 
Sriram Raghavan and Hector Garcia-Molina (2001). "Crawling the Hidden Web". In Proceedings of the 27th International Conference on Very Large Data Bases (VLDB): 129-138. 
Nigel Hamilton (2003). The Mechanics of a Deep Net Metasearch Engine - 12th World Wide Web Conference poster.
Bin He and Kevin Chen-Chuan Chang (2003). "Statistical Schema Matching across Web Query Interfaces". In Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data. 
Joe Barker (Jan 2004). Invisible Web: What it is, Why it exists, How to find it, and Its inherent ambiguity UC Berkeley - Teaching Library Internet Workshops.
Alex Wright (Mar 2004). In Search of the Deep Web, Salon.com, http://www.salon.com/tech/feature/2004/03/09/deep_web/
Alexandros Ntoulas, Petros Zerfos, and Junghoo Cho (2005). "Downloading Textual Hidden Web Content Through Keyword Queries". In Proceedings of the Joint Conference on Digital Libraries (JCDL): 100-109.  Extended version
Frank McCown, Xiaoming Liu, Michael L. Nelson, and Mohammad Zubair (Mar/Apr 2006). "Search Engine Coverage of the OAI-PMH Corpus". IEEE Internet Computing 10 (2): 66-73. 
Steve Gruchawka (June 2006). How-To Guide to the Deep Web TechDeepWeb.com, http://TechDeepWeb.com
Bin He, Mitesh Patel, Zhen Zhang, and Kevin Chen-Chuan Chang (May 2007). "Accessing the Deep Web: A Survey". Communications of the ACM (CACM) 50 (2): 94-101.