The deep Web (also called Deepnet, the invisible Web, or the hidden Web) refers to World Wide Web content that is not part of the surface Web, which is indexed by search engines. It is estimated that the deep Web is several orders of magnitude larger than the surface Web.
Password-protected log-in required databases are a type of deep web, which includes almost all subscription based scholarly databases. Libraries subscribe to a number of databases and users have to visit each database and use its own search engine. Some libraries subscribe to more than one hundred databases and it is cumbersome for users to visit each database. Library communities, together with information technology communities, are trying to develop a federated search engine which can index all subscribed databases and retrieve relevant information with one search query. They are hoping to have one search engine that can search and retrieve all available information sources to each library, that includes library online catalog, subscribed databases, and free web sources.
Michael Bergman mentioned that Jill Ellsworth used the term "invisible Web" in 1994 to refer to websites that are 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 (Director of Product Development) and Matthew B. Koll (CEO/Founder) of Personal Library Software, Inc. (PLS) when describing the @1 deep Web tool. The term was used in a December 1996 press release from PLS. The first use of the specific term deep Web occurred in that same 2001 Bergman study.
The first commercial deep Web tool was @1 from Personal Library Software (PLS), announced December 12, 1996 in partnership with large content providers. According to a December 12, 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. PLS was acquired by AOL in 1998 and @1 was abandoned.
In 2000, it was estimated that the deep Web contained approximately 7,500 terabytes of data and 550 billion individual documents. Estimates – based on extrapolations from a study done at University of California, Berkeley – show 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 in total both invisible and surface web combined.
Deep Web resources may be classified into one or more of the following categories:
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 access the deep Web is via federated search based search engines. Search tools such as Science.gov are being designed to retrieve information from the deep Web. These tools identify and interact with searchable databases, aiming to provide access to deep Web content.
Another way to explore the deep Web is by using human crawlers instead of algorithmic crawlers. In this paradigm referred to as Web harvesting, humans find interesting links within the deep Web that algorithmic crawlers otherwise may not find. This human-based computation technique to discover the deep Web has been used by the 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 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.
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.
Federated search by subject category or vertical is an alternative mechanism to crawling the deep Web. Traditional engines have difficulty crawling and indexing deep Web pages and their content, but deep Web search engines like CloserLookSearch, Science.gov 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.
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.
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. 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. 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 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.
Libraries subscribe to a number of separate databases, which are technically deep web. Each database holds thousands of journal titles, reference materials, and other information. Each database has its own search engine and is indexed separately.
Current information retrieval mechanism at libraries do not allow users to search multiple databases with one search engine. Users have to visit each database, use its own search engine, and find material separately. Some academic libraries are subscribing nearly one hundred or more separate databases and users have to visit each database separately.
In academic libraries, students tend to avoid such cumbersome search process and use general search engines such as Google. General search engines, however, do not retrieve pages inside academic databases students are expected to use. To avoid this problem, libraries are seeking a better information retrieval mechanism that allow users to retrieve relevant information across databases with one federated search engine, or similar search engine, that can index all databases (deep web) each library is subscribing to, as well as library online catalogs. In other words, they are trying to develop one federated search engine that can search all information sources including subscription based databases, free web sources, and library catalogs.
Google Scholar, Google's search engine for scholarly literature, makes arrangement with publishers, and index journal articles held by those publishers. Thus, users can find limited journal articles on the web by Google Scholar.
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