Discover

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).^[1]
Less commonly, the term deep Web may represent ''deeper interaction''.

Contents

Size

Deep resources

Accessing

Crawling the deep Web

Classifying resources

History

References

See also

External links

Size

In 2000, it was estimated that the deep Web contained approximately 7,500 terabytes of data and 550 billion individual documents (Bergman, 2001). Indiana University faculty member Javed Mustafa appeared on National Public Radio's Science Friday program on July 27, 2007, and drawing on information in a published study from University of California, Berkeley entitled ''How much information is there?'', estimated 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 contains about 11 terabytes, for comparison. Mustafa noted that these numbers were a bit dated and were just rough estimates.^[2][3]

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 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.

Accessing

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.
One way to explore the deep web is by using human crawlers instead of algorithmic crawlers. Humans can find interesting links of the deep web that algorithmic crawlers can't find. This human-based computation technique to discover the deep web was used by StumbleUpon service since February 2002.
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.

Crawling the deep Web

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.

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 since the resource could have been found using the Sitemap Protocol, mod_oai, OAIster, etc. 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.
The concept of classifying search results by topic was pioneered by Yahoo! Directory search and is gaining importance as search becomes more relevant in day to day decisions. However, most of the work here has been in categorizing the surface Web by topic. There is little pioneering work done on the deep Web in this area. 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 (health, travel, automobiles, etc.) and sub-topics according to the nature of the content underlying their databases. Several deep Web directories are under development such as OAIster by the University of Michigan, INFOMINE at the University of California at Riverside and DirectSearch by Gary Price to name a few.
The second, 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 URL's 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.

History

In 1994, Dr. Jill Ellsworth was the first to coin the term "Invisible Web" (Bergman, 2001). In a January 1996 article, Ellsworth states:

"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'."

The first commercial deep web tool (although they referred to it as the "Invisible Web") was @1, announced December 12th, 1996 in partnership with large content providers. According to a December 12th, 1996 press release, @1 started with 5.7 terabytes of content which was estimated to be 30 times the size of the nascent World Wide Web.^[4]
Another early use of the term "invisible web" was by Bruce Mount (Director of Product Development) and Dr. Matthew B. Koll (CEO/Founder) of PLS when describing @1 to the public. PLS was acquired by AOL in 1998 and @1 was abandoned.

References

1. ''Business and Marketing on the Internet'', Frank Garcia, 1996.
2. ''Hour Two: Depression Medication / Baby Talk / Search Engines'', Science Friday, National Public Radio, July 27, 2007
3. The unpublished paper ''How much information is there in the world?'', by Michael Lesk in 1997, estimated that in 1997, the Library of Congress had between 20 terabytes and 3 petabytes.
4. AOL (Dec 1996) press release ''announcing AOL's participation in @1''


★ Personal Library Software (Dec 1996) press release ''announcing @1 as an "Invisible Web" search service''

★

★ Gary Price & Chris Sherman (July 2001). ''The Invisible Web : Uncovering Information Sources Search Engines Can't See.'' CyberAge Books, ISBN 0-910965-51-X.

★ The Deep Web: Surfacing Hidden Value, Michael K. Bergman, , , The Journal of Electronic Publishing, 2001

★

★ Nigel Hamilton (2003). ''The Mechanics of a Deep Net Metasearch Engine'' - 12th World Wide Web Conference poster.

★

★ 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/

★ Extended version

★ Search Engine Coverage of the OAI-PMH Corpus, Frank McCown, Xiaoming Liu, Michael L. Nelson, and Mohammad Zubair, , , IEEE Internet Computing, 2006

★ Steve Gruchawka (June 2006). ''How-To Guide to the Deep Web'' TechDeepWeb.com, http://TechDeepWeb.com

★ Accessing the Deep Web: A Survey, Bin He, Mitesh Patel, Zhen Zhang, and Kevin Chen-Chuan Chang, , , Communications of the ACM (CACM), 2007

See also

★ Robots Exclusion Standard

★ Surface Web

★ Web crawler

External links

DIET: DIET (''Deep Information and Extraction Tool'') is a web tool developed by a research team at Ecole Polytechnique de Montréal, which has been commercialized by Univalor. It permits a user to find information residing in the deep web, extract it and manipulate it.
Travel industry and Deep Web interview with 'Marcus P. Zillman', International information retrieval, Deep Web and Internet expert.