The deep Web (or Deepnet, invisible Web or hidden Web) refers to World Wide Web content not part of the surface Web indexed by search engines. The first known (so far) use of the term “Invisible Web” dates from a December 12th, 1996 press release from Personal Library Software (PLS) as they released their @1 “invisible web” search service.

Less commonly, the term deep Web may represent deeper interaction.

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.
• 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).
• Limited access content - sites that require registration or otherwise limit access to their pages (e.g., using the Robots Exclusion Standard), prohibiting search engines from browsing them and creating cached copies.
• Scripted content - pages that are only accessible through links produced by JavaScript and Flash which require special handling.
• Non-text content - multimedia (image) files, Usenet archives and documents in non-HTML file formats such as PDF and DOC documents (although some search engines, such as Google, are able to index these types of files).

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.

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 Alacra, 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 by Google’s Sitemap Protocol, mod_oai, OAIster, etc. If a search engine provides a backlink for a resource, we 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, 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

The first commercial deep web tool (although they referred to it as the “Invisible Web”) was @1, announced December 12th, 1996 in parnership with large content providers. According to the 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. The term “invisible web” was coined by Bruce Mount (Director of Product Development) and Dr. Matthew B. Koll (CEO/Founder) of PLS for use in describing @1 to the public. PLS was acquired by AOL in 1998 and @1 was abandoned.

Source & Further Reading:
http://en.wikipedia.org/wiki/Invisible_web