Deep Web from Wikipedia

From Wikipedia, the free encyclopedia

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 orders of magnitude larger than the surface Web.
Contents

* 1 Naming
* 2 Size
* 3 Deep resources
* 4 Accessing
* 5 Crawling the deep Web
* 6 Classifying resources
* 7 History
* 8 See also
* 9 References
* 10 Further reading
* 11 External links

Naming

Michael Bergman has said that Jill Ellsworth coined the term "invisible Web" in 1994 to refer to websites that are not registered with any search engine.[1] Bergman cited a January 1996 article by Frank Garcia in which Ellsworth was quoted using the term (but did not say she coined it in 1994):[2]

"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.[citation needed] The term was used in a December 1996 press release from PLS.[3]

The first use of the specific term 'deep Web' occurred in that same 2001 Bergman study.[1]

Size

In 2000, it was estimated that the deep Web contained approximately 7,500 terabytes of data and 550 billion individual documents.[1] Estimates, based on extrapolations from the study entitled How much information is there?, from 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, for comparison.[4][5]

[edit] Deep resources

Deep Web resources may be classified into one or more of the following categories[citation needed]:

* 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 access the deep Web is via federated search based search engines. Search tools such as Science.gov and Pipl - People Search 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 of the deep Web that algorithmic crawlers can't 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.

Some vertical search tools such as Pipl are being designed to retrieve information from the deep Web; their crawlers identify and 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.

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.

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. 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 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.

History

The first commercial deep Web tool was @1 from Personal Library Software (PLS), 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.[6] PLS was acquired by AOL in 1998 and @1 was abandoned.

See also

* Federated search
* Robots Exclusion Standard
* Surface Web
* Web crawler
* Web Harvesting
* Dark internet
* Darknet

References

1. ^ a b c d Bergman, Michael K. (Aug 2001). "The Deep Web: Surfacing Hidden Value". The Journal of Electronic Publishing 7 (1).. According to that paper, the study was originally published on July 26, 2000, with data then updated to 2001.
2. ^ Garcia, Frank (January 1996). "Business and Marketing on the Internet". Masthead 9 (1). (Citation from Flynn-Burhoe, Maureen (19 December 2006). "The Ultimate Guide to the Invisible Web". oceanflynn @ Digg.) (Electronic copy archived by the Internet Archive.)
3. ^ Personal Library Software (Dec 1996). "PLS introduces AT1, the first 'second generation' Internet search service". (Archived by the Internet Archive.)
4. ^ Hour Two: Depression Medication / Baby Talk / Search Engines, Science Friday, National Public Radio, July 27, 2007
5. ^ 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 200 terabytes and 3 petabytes.
6. ^ AOL (Dec 1996) press release announcing AOL's participation in @1

Further reading

* Barker, Joe (Jan 2004). Invisible Web: What it is, Why it exists, How to find it, and Its inherent ambiguity UC Berkeley - Teaching Library Internet Workshops.
* Bergman, Michael K. (Aug 2001). "The Deep Web: Surfacing Hidden Value". The Journal of Electronic Publishing 7 (1).
* Gruchawka, Steve (June 2006). How-To Guide to the Deep Web TechDeepWeb.com, http://TechDeepWeb.com
* Hamilton, Nigel (2003). The Mechanics of a Deep Net Metasearch Engine - 12th World Wide Web Conference poster.
* He, Bin; Chang, Kevin Chen-Chuan (2003). "Statistical Schema Matching across Web Query Interfaces". Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data.
* He, Bin; Patel, Mitesh; Zhang, Zhen; Chang, Kevin Chen-Chuan (May 2007). "Accessing the Deep Web: A Survey". Communications of the ACM (CACM) 50 (2): 94-101.
* Ipeirotis, Panagiotis G.; Gravano, Luis; Sahami, Mehran (2001). "Probe, Count, and Classify: Categorizing Hidden-Web Databases". Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data: 67-78.
* King, John D.; Li, Yuefeng; Tao, Daniel; Nayak, Richi (November 2007). "Mining World Knowledge for Analysis of Search Engine Content". Web Intelligence and Agent Systems: An International Journal 5 (3): 233-253.
* McCown, Frank; Liu, Xiaoming; Nelson, Michael L.; Zubair, Mohammad (Mar/Apr 2006). "Search Engine Coverage of the OAI-PMH Corpus". IEEE Internet Computing 10 (2): 66-73.
* Ntoulas, Alexandros; Zerfos, Petros; Cho, Junghoo (2005). "Downloading Textual Hidden Web Content Through Keyword Queries". Proceedings of the Joint Conference on Digital Libraries (JCDL): 100-109. Extended version
* Personal Library Software (Dec 1996) press release announcing @1 as an "Invisible Web" search service
* Price, Gary; Sherman, Chris (July 2001). The Invisible Web : Uncovering Information Sources Search Engines Can't See. CyberAge Books. ISBN 0-910965-51-X.
* Raghavan, Sriram; Garcia-Molina, Hector (2001). "Crawling the Hidden Web". Proceedings of the 27th International Conference on Very Large Data Bases (VLDB): 129-138.
* Wright, Alex (Mar 2004). In Search of the Deep Web, Salon.com, http://www.salon.com/tech/feature/2004/03/09/deep_web/

Deep Search Application Download

You can download Deep Search from two places

1: http://code.google.com/p/deepsearch/downloads/list

there is a setup file, zip file and the zip file containing the source code

2: If you have Internet Explorer you can just browse to here


http://www.newirelandcomputing.com/DeepSearch/index.html

click on install

Its the beta release there is no error handling code yet, still testing so if you find any bugs please let me know so i can improve it.

Patrick Lismore

Deep Search Project

I started this project so that Internet users could really understand that the Internet is a lot more than just the results they get from search engines. I decided to design and develop a Windows based application that would allow users to query the web like normal but get very different results. The deep web or Deepnet, invisible Web or the hidden Web is not the web you see when you query a search engine. Results from normal search engines are part of the surface Web, that is the part of the web indexed by search engines for normal browsing. It has been estimated that the deep net is several orders of magnitude larger than the general exposed surface Web so using C# , .Net 2.0 and some of Google's advanced operators it is possible to come up with some astonishing results. Everyone knows how good Google is at searching the web and providing excellent results but Google is capable of much much more. Harnessing the power of a desktop application its possible to query Google's search engine to return deep web results. This application aims to enhance peoples understanding of the real web and its possibilities as well as help them find the exact file or document they are looking for that is if they are not be able to find it on the surface web through normal search engines.

Patrick Lismore