Deep Web
Deep Web (also called the Deepnet, Invisible Web, or Hidden Web) is World Wide Web content that is not part of the Surface Web, which is indexed by standard search engines. It should not be confused with the dark Internet, the computers that can no longer be reached via the Internet, or with a Darknet distributed filesharing network, which could be classified as a smaller part of the Deep Web. Some prosecutors and government agencies think that the Deep Web is a haven for serious criminality.
Mike Bergman, founder of BrightPlanet and credited with coining the phrase, 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. As of 2001, the deep Web was several orders of magnitude larger than the surface Web.
Contents
Size
Bright Planet, a web-services company, describes the size of the Deep Web in this way:
It is impossible to measure or put estimates onto the size of the deep web because the majority of the information is hidden or locked inside databases. Early estimates suggested that the deep web is 4,000 to 5,000 times larger than the surface web. However, since more information and sites are always being added, it can be assumed that the deep web is growing exponentially at a rate that cannot be quantified. Estimates based on extrapolations from a study done at University of California, Berkeley in 2001 speculate that the deep web consists of about 7.5 petabytes. More accurate estimates are available for the number of resources in the deep Web: research of He et al. detected around 300,000 deep web sites in the entire Web in 2004, and, according to Shestakov, around 14,000 deep web sites existed in the Russian part of the Web in 2006.
Naming
Bergman, in a seminal 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 were not registered with any search engine. Bergman cited a January 1996 article by Frank Garcia:
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.
The first use of the specific term Deep Web, now generally accepted, occurred in the aforementioned 2001 Bergman study.
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).
- Limited access content: sites that limit access to their pages in a technical way (e.g., using the Robots Exclusion Standard or CAPTCHAs, or no-store directive which prohibit 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 the Deep Web
While it is not always possible to discover a specific web server’s external IP address, theoretically almost any site can be accessed via its IP address, regardless of whether or not it has been indexed.
Certain content is intentionally hidden from the regular internet, accessible only with special software, such as Tor. Tor allows users to access websites using the .onion host suffix anonymously, hiding their IP address. Other such software includes I2P and Freenet.
In 2008, in order to facilitate user access and search engine indexing of hidden services using the .onion suffix, Aaron Swartz designed Tor2web, a proxy application able to provide access to Tor hidden services by means of common web browsers.
To discover content on the Web, search engines use web crawlers that follow hyperlinks through known protocol virtual port numbers. 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 indeterminate 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 for a member of the deep Web.
DeepPeep, Intute, Deep Web Technologies, Scirus, and Ahmia.fi are a few search engines that have accessed the Deep Web. Intute ran out of funding and is now a temporary static archive as of July, 2011. Scirus retired near the end of January, 2013.
Crawling the Deep Web
Researchers have been exploring how the Deep Web can be crawled in an automatic fashion, including content that can be accessed only by special software such as Tor. In 2001, Sriram Raghavan and Hector Garcia-Molina (Stanford Computer Science Department, Stanford University) 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. Several form query languages (e.g., DEQUEL) have been proposed that, besides issuing a query, also allow to extract structured data from result pages. 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.
Commercial search engines have begun exploring alternative methods to crawl the deep Web. The Sitemap Protocol (first developed, and introduced by Google in 2005) 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. In this system, the pre-computation of submissions is done using three algorithms:
selecting input values for text search inputs that accept keywords, identifying inputs which accept only values of a specific type (e.g., date), and selecting a small number of input combinations that generate URLs suitable for inclusion into the Web search index.
Classifying resources
Automatically determining if a Web resource is a member of the surface Web or the deep Web is difficult. 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. Furthermore, a resource may reside in the surface Web even though it has yet to be found by a search engine.
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. 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 OAIster at the University of Michigan, Intute at the University of Manchester, Infomine 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 special, 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 beneath 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.