Deep Web

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.

Naming

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.

Size

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 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.

• Password protected Web – sites that require registration and login; includes almost all paid subscription databases such as academic databases (they are basically dynamic web).

• 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).

• 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 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.

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, 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.

Deep web (database) and libraries

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.

See also

• Database
• Index (search engine)
• Web search engine

References

ISBN links support NWE through referral fees

• 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. Retrieved November 16, 2008.
• Bergman, Michael K., The Deep Web: Surfacing Hidden Value. The Journal of Electronic Publishing 7 (1)(August 2001) [2]. Retrieved November 16, 2008.
• Gruchawka, Steve (June 2006). How-To Guide to the Deep Web TechDeepWeb.com. Retrieved November 16, 2008.
• Hamilton, Nigel (2003). The Mechanics of a Deep Net Metasearch Engine - 12th World Wide Web Conference poster. Retrieved November 16, 2008.
• He, Bin, and Kevin Chen-Chuan Chang, 2003 Statistical Schema Matching across Web Query Interfaces. Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data. [3]. Retrieved November 16, 2008.
• Ipeirotis, Panagiotis G., Luis Gravano, Mehran Sahami, (2001). "Probe, Count, and Classify: Categorizing Hidden-Web Databases." Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data, 67-78. Retrieved November 16, 2008.
• King, John D. and Yuefeng Li, Daniel Tao, Richi Nayak, (November 2007). Mining World Knowledge for Analysis of Search Engine Content. Web Intelligence and Agent Systems: An International Journal 5 (3): 233–253. Retrieved November 16, 2008.
• McCown, Frank and Xiaoming Liu, Michael L. Nelson, Mohammad Zubair, (Mar/Apr 2006). Search Engine Coverage of the OAI-PMH Corpus. IEEE Internet Computing 10 (2): 66–73. Retrieved November 16, 2008.
• Ntoulas, Alexandros; Petros Zerfos, Junghoo Cho, (2005). "Downloading Textual Hidden Web Content Through Keyword Queries." Proceedings of the Joint Conference on Digital Libraries (JCDL), 100-109. Extended version Retrieved November 16, 2008.
• Personal Library Software (Dec 1996) press release announcing @1 as an "Invisible Web" search service (archived page on Internet Archives). Retrieved November 16, 2008.
• Price, Gary and Chris Sherman, (July 2001). The Invisible Web : Uncovering Information Sources Search Engines Can't See. CyberAge Books. ISBN 091096551X.
• Raghavan, Sriram; Hector Garcia-Molina, (2001). "Crawling the Hidden Web." Proceedings of the 27th International Conference on Very Large Data Bases (VLDB), 129-138. Retrieved November 16, 2008.
• Shestakov, Denis. (June 2008). Search Interfaces on the Web: Querying and Characterizing. TUCS Doctoral Dissertations 104, University of Turku. Retrieved November 16, 2008.
• Wright, Alex (Mar 2004). In Search of the Deep Web, Salon.com. Retrieved November 16, 2008.

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