Deep Web

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The deep Web (also called Deepnet, the invisible Web, dark Web or the hidden Web) refers to World Wide Web content that is not part of the surface Web, which is indexed by standard search engines.

Mike Bergman, credited with coining the phrase,[1] has said that searching on the Internet today can be compared to dragging a net across the surface of the ocean; a great deal may be caught in the net, but there is a wealth of information that is deep and therefore missed. Most of the Web's information is buried far down on dynamically generated sites, and standard search engines do not find it. Traditional search engines cannot "see" or retrieve content in the deep Web – those pages do not exist until they are created dynamically as the result of a specific search. The deep Web is several orders of magnitude larger than the surface Web.[2]



Bergman, in a seminal, early paper on the deep Web published in the Journal of Electronic Publishing, mentioned that Jill Ellsworth used the term invisible Web in 1994 to refer to websites that are not registered with any search engine.[2] Bergman cited a January 1996 article by Frank Garcia:[3]

"It would be a site that's possibly reasonably designed, but they didn't bother to register it with any of the search engines. So, no one can find them! You're hidden. I call that the invisible Web."

Another early use of the term invisible Web was by Bruce Mount and Matthew B. Koll of Personal Library Software, in a description of the @1 deep Web tool found in a December 1996 press release.[4]

The first use of the specific term deep Web, now generally accepted, occurred in the aforementioned 2001 Bergman study.[2]


In 2000, it was estimated that the deep Web contained approximately 7,500 terabytes of data and 550 billion individual documents.[2] Estimates based on extrapolations from a study done at University of California, Berkeley, speculate that the deep Web consists of about 91,000 terabytes. By contrast, the surface Web (which is easily reached by search engines) is only about 167 terabytes; the Library of Congress, in 1997, was estimated to have perhaps 3,000 terabytes.[5]

Deep resources

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 (such as 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).
  • 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.


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.[1] 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 subscription websites display their full content to search engine robots so they will show up in user searches, but then show users a login or subscription page when they click a link from the search engine results page.

Crawling the deep Web

Researchers have been exploring how the deep Web can be crawled in an automatic fashion. In 2001, Sriram Raghavan and Hector Garcia-Molina[7][8] 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. Alexandros Ntoulas, Petros Zerfos, and Junghoo Cho of UCLA created a hidden-Web crawler that automatically generated meaningful queries to issue against search forms.[9] Their crawler generated promising results, but the problem is far from being solved, as the authors recognized. Another effort is DeepPeep, a project of the University of Utah sponsored by the National Science Foundation, which gathered hidden-Web sources (Web forms) in different domains based on novel focused crawler techniques.[10] [11]

Commercial search engines have begun exploring alternative methods to crawl the deep Web. The Sitemap Protocol (first developed by Google) 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. Google's deep Web surfacing system pre-computes submissions for each HTML form and adds the resulting HTML pages into the Google search engine index. The surfaced results account for a thousand queries per second to deep Web content.[12]. In this system, the pre-computation of submissions is done using three algorithms: (1) selecting input values for text search inputs that accept keywords, (2) identifying inputs which accept only values of a specific type (e.g., date), and (3) selecting a small number of input combinations that generate URLs suitable for inclusion into the Web search index.

Classifying resources

It is difficult to automatically determine if a Web resource is a member of the surface Web or the deep Web. If a resource is indexed by a search engine, it is not necessarily a member of the surface Web, because the resource could have been found using another method (e.g., the Sitemap Protocol, mod_oai, OAIster) instead of traditional crawling. If a search engine provides a backlink for a resource, one may assume that the resource is in the surface Web. Unfortunately, search engines do not always provide all backlinks to resources. Even if a backlink does exist, there is no way to determine if the resource providing the link is itself in the surface Web without crawling all of the Web. Furthermore, a resource may reside in the surface Web, but it has not yet been found by a search engine. Therefore, if we have an arbitrary resource, we cannot know for sure if the resource resides in the surface Web or deep Web without a complete crawl of the Web.

Most of the work of classifying search results has been in categorizing the surface Web by topic. For classification of deep Web resources, Ipeirotis et al.[13] presented an algorithm that classifies a deep Web site into the category that generates the largest number of hits for some carefully selected, topically-focused queries. Deep Web directories under development include as OAIster at the University of Michigan, Intute at the University of Manchester, Infomine[14] at the University of California at Riverside, and DirectSearch (by Gary Price). This classification poses a challenge while searching the deep Web whereby two levels of categorization are required. The first level is to categorize sites into vertical topics (e.g., health, travel, automobiles) and sub-topics according to the nature of the content underlying their databases.

The more difficult challenge is to categorize and map the information extracted from multiple deep Web sources according to end-user needs. Deep Web search reports cannot display URLs like traditional search reports. End users expect their search tools to not only find what they are looking for quickly, but to be intuitive and user-friendly. In order to be meaningful, the search reports have to offer some depth to the nature of content that underlie the sources or else the end-user will be lost in the sea of URLs that do not indicate what content lies underneath them. The format in which search results are to be presented varies widely by the particular topic of the search and the type of content being exposed. The challenge is to find and map similar data elements from multiple disparate sources so that search results may be exposed in a unified format on the search report irrespective of their source.


The lines between search engine content and the deep Web have begun to blur, as search services start to provide access to part or all of once-restricted content. An increasing amount of deep Web content is opening up to free search as publishers and libraries make agreements with large search engines. In the future, deep Web content may be defined less by opportunity for search than by access fees or other types of authentication.[15]

See also


  1. 1.0 1.1 Wright, Alex (2009-02-22). "Exploring a 'Deep Web' That Google Can’t Grasp". New York Times. Retrieved 2009-02-23. 
  2. 2.0 2.1 2.2 2.3 Bergman, Michael K. (August 2001). "The Deep Web: Surfacing Hidden Value". The Journal of Electronic Publishing 7 (1). doi:10.3998/3336451.0007.104.;view=text;rgn=main;idno=3336451.0007.104. 
  3. Garcia, Frank (January 1996). "Business and Marketing on the Internet". Masthead 9 (1). Retrieved 2009-02-24. 
  4. @1 started with 5.7 terabytes of content, estimated to be 30 times the size of the nascent World Wide Web; PLS was acquired by AOL in 1998 and @1 was abandoned. Personal Library Software (December 1996). "PLS introduces AT1, the first 'second generation' Internet search service". Press release. Retrieved 2009-02-24. 
  5. Michael, Lesk. How much information is there in the world?. Retrieved 2009-02-24. 
  6. pragma:no-cache/cache-control:no-cache "HTTP 1.1: Header Field Definitions (14.32 Pragma)". HTTP - Hypertext Transfer Protocol. W3C. 1999. pragma:no-cache/cache-control:no-cache. Retrieved 2009-02-24. 
  7. Sriram Raghavan; Hector Garcia-Molina (2000) (PDF). Crawling the Hidden Web. Stanford Digital Libraries Technical Report. Retrieved 2008-12-27. 
  8. Raghavan, Sriram; Garcia-Molina, Hector (2001). "Crawling the Hidden Web" (PDF). Proceedings of the 27th International Conference on Very Large Data Bases (VLDB). pp. 129–138. 
  9. Alexandros, Ntoulas; Petros Zerfos, and Junghoo Cho (2005). Downloading Hidden Web Content. UCLA Computer Science. Retrieved 2009-02-24. 
  10. Barbosa, Luciano; Juliana Freire (2007). An Adaptive Crawler for Locating Hidden-Web Entry Points. WWW Conference 2007. Retrieved 2009-03-20. 
  11. Barbosa, Luciano; Juliana Freire (2005). Searching for Hidden-Web Databases.. WebDB 2005. Retrieved 2009-03-20. 
  12. Madhavan, Jayant; David Ko, Łucja Kot, Vignesh Ganapathy, Alex Rasmussen, Alon Halevy (2008). Google’s Deep-Web Crawl. VLDB Endowment, ACM. Retrieved 2009-04-17. 
  13. Ipeirotis, Panagiotis G.; Gravano, Luis; Sahami, Mehran (2001). "Probe, Count, and Classify: Categorizing Hidden-Web Databases" (PDF). Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data. pp. 67–78. 
  15. Cohen, Laura. "Internet Tutorials: The Deep Web". Retrieved 2009-01-16. 

Further reading

External links

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