Web search engine
A web search engine is a software system that is designed to search for information on the World Wide Web. The search results are generally presented in a line of results often referred to as search engine results pages (SERPs). The information may be a mix of web pages, images, and other types of files. Some search engines also mine data available in databases or open directories. Unlike web directories, which are maintained only by human editors, search engines also maintain real-time information by running an algorithm on a web crawler.
|Timeline (full list)|
|Go.com||Active, Yahoo Search|
|1995||AltaVista||Inactive, redirected to Yahoo!|
|Yahoo! 2008||Active, Launched as a directory|
|Inktomi||Inactive, acquired by Yahoo!|
|Ask Jeeves||Active (rebranded ask.com)|
|Ixquick||Active also as Startpage|
|MSN Search||Active as Bing|
|empas||Inactive (merged with NATE)|
|1999||AlltheWeb||Inactive (URL redirected to Yahoo!)|
|GenieKnows||Active, rebranded Yellowee.com|
|2004||Yahoo! Search||Active, Launched own web search
(see Yahoo! Directory, 1995)
|Soso (search engine)||Active|
|Live Search||Active as Bing, Launched as
rebranded MSN Search
|Blackle.com||Active, Google Search|
|2008||Powerset||Inactive (redirects to Bing)|
|Forestle||Inactive (redirects to Ecosia)|
|2009||Bing||Active, Launched as
rebranded Live Search
|Mugurdy||Inactive due to a lack of funding|
|Yandex||Active, Launched global
|2011||YaCy||Active, P2P web search engine|
During early development of the web, there was a list of webservers edited by Tim Berners-Lee and hosted on the CERN webserver. One historical snapshot of the list in 1992 remains, but as more and more webservers went online the central list could no longer keep up. On the NCSA site, new servers were announced under the title "What's New!"
The very first tool used for searching on the Internet was Archie. The name stands for "archive" without the "v". It was created in 1990 by Alan Emtage, Bill Heelan and J. Peter Deutsch, computer science students at McGill University in Montreal. The program downloaded the directory listings of all the files located on public anonymous FTP (File Transfer Protocol) sites, creating a searchable database of file names; however, Archie did not index the contents of these sites since the amount of data was so limited it could be readily searched manually.
The rise of Gopher (created in 1991 by Mark McCahill at the University of Minnesota) led to two new search programs, Veronica and Jughead. Like Archie, they searched the file names and titles stored in Gopher index systems. Veronica (Very Easy Rodent-Oriented Net-wide Index to Computerized Archives) provided a keyword search of most Gopher menu titles in the entire Gopher listings. Jughead (Jonzy's Universal Gopher Hierarchy Excavation And Display) was a tool for obtaining menu information from specific Gopher servers. While the name of the search engine "Archie" was not a reference to the Archie comic book series, "Veronica" and "Jughead" are characters in the series, thus referencing their predecessor.
In the summer of 1993, no search engine existed for the web, though numerous specialized catalogues were maintained by hand. Oscar Nierstrasz at the University of Geneva wrote a series of Perl scripts that periodically mirrored these pages and rewrote them into a standard format. This formed the basis for W3Catalog, the web's first primitive search engine, released on September 2, 1993.
In June 1993, Matthew Gray, then at MIT, produced what was probably the first web robot, the Perl-based World Wide Web Wanderer, and used it to generate an index called 'Wandex'. The purpose of the Wanderer was to measure the size of the World Wide Web, which it did until late 1995. The web's second search engine Aliweb appeared in November 1993. Aliweb did not use a web robot, but instead depended on being notified by website administrators of the existence at each site of an index file in a particular format.
JumpStation (created in December 1993 by Jonathon Fletcher) used a web robot to find web pages and to build its index, and used a web form as the interface to its query program. It was thus the first WWW resource-discovery tool to combine the three essential features of a web search engine (crawling, indexing, and searching) as described below. Because of the limited resources available on the platform it ran on, its indexing and hence searching were limited to the titles and headings found in the web pages the crawler encountered.
One of the first "all text" crawler-based search engines was WebCrawler, which came out in 1994. Unlike its predecessors, it allowed users to search for any word in any webpage, which has become the standard for all major search engines since. It was also the first one widely known by the public. Also in 1994, Lycos (which started at Carnegie Mellon University) was launched and became a major commercial endeavor.
Soon after, many search engines appeared and vied for popularity. These included Magellan, Excite, Infoseek, Inktomi, Northern Light, and AltaVista. Yahoo! was among the most popular ways for people to find web pages of interest, but its search function operated on its web directory, rather than its full-text copies of web pages. Information seekers could also browse the directory instead of doing a keyword-based search.
Google adopted the idea of selling search terms in 1998, from a small search engine company named goto.com. This move had a significant effect on the SE business, which went from struggling to one of the most profitable businesses in the internet.
In 1996, Netscape was looking to give a single search engine an exclusive deal as the featured search engine on Netscape's web browser. There was so much interest that instead Netscape struck deals with five of the major search engines: for $5 million a year, each search engine would be in rotation on the Netscape search engine page. The five engines were Yahoo!, Magellan, Lycos, Infoseek, and Excite.
Search engines were also known as some of the brightest stars in the Internet investing frenzy that occurred in the late 1990s. Several companies entered the market spectacularly, receiving record gains during their initial public offerings. Some have taken down their public search engine, and are marketing enterprise-only editions, such as Northern Light. Many search engine companies were caught up in the dot-com bubble, a speculation-driven market boom that peaked in 1999 and ended in 2001.
Around 2000, Google's search engine rose to prominence. The company achieved better results for many searches with an innovation called PageRank, as was explained in Anatomy of a Search Engine. This iterative algorithm ranks web pages based on the number and PageRank of other web sites and pages that link there, on the premise that good or desirable pages are linked to more than others. Google also maintained a minimalist interface to its search engine. In contrast, many of its competitors embedded a search engine in a web portal. In fact, Google search engine became so popular that spoof engines emerged such as Mystery Seeker.
By 2000, Yahoo! was providing search services based on Inktomi's search engine. Yahoo! acquired Inktomi in 2002, and Overture (which owned AlltheWeb and AltaVista) in 2003. Yahoo! switched to Google's search engine until 2004, when it launched its own search engine based on the combined technologies of its acquisitions.
Microsoft first launched MSN Search in the fall of 1998 using search results from Inktomi. In early 1999 the site began to display listings from Looksmart, blended with results from Inktomi. For a short time in 1999, MSN Search used results from AltaVista were instead. In 2004, Microsoft began a transition to its own search technology, powered by its own web crawler (called msnbot).
How web search engines work
||This section possibly contains original research. (October 2012)|
|This article needs additional citations for verification. (July 2013)|
A search engine operates in the following order:
Web search engines work by storing information about many web pages, which they retrieve from the HTML markup of the pages. These pages are retrieved by a Web crawler (sometimes also known as a spider) — an automated Web crawler which follows every link on the site. The site owner can exclude specific pages by using robots.txt.
The search engine then analyzes the contents of each page to determine how it should be indexed (for example, words can be extracted from the titles, page content, headings, or special fields called meta tags). Data about web pages are stored in an index database for use in later queries. A query from a user can be a single word. The index helps find information relating to the query as quickly as possible. Some search engines, such as Google, store all or part of the source page (referred to as a cache) as well as information about the web pages, whereas others, such as AltaVista, store every word of every page they find. This cached page always holds the actual search text since it is the one that was actually indexed, so it can be very useful when the content of the current page has been updated and the search terms are no longer in it. This problem might be considered a mild form of linkrot, and Google's handling of it increases usability by satisfying user expectations that the search terms will be on the returned webpage. This satisfies the principle of least astonishment, since the user normally expects that the search terms will be on the returned pages. Increased search relevance makes these cached pages very useful as they may contain data that may no longer be available elsewhere.
When a user enters a query into a search engine (typically by using keywords), the engine examines its index and provides a listing of best-matching web pages according to its criteria, usually with a short summary containing the document's title and sometimes parts of the text. The index is built from the information stored with the data and the method by which the information is indexed. From 2007 the Google.com search engine has allowed one to search by date by clicking "Show search tools" in the leftmost column of the initial search results page, and then selecting the desired date range. Most search engines support the use of the boolean operators AND, OR and NOT to further specify the search query. Boolean operators are for literal searches that allow the user to refine and extend the terms of the search. The engine looks for the words or phrases exactly as entered. Some search engines provide an advanced feature called proximity search, which allows users to define the distance between keywords. There is also concept-based searching where the research involves using statistical analysis on pages containing the words or phrases you search for. As well, natural language queries allow the user to type a question in the same form one would ask it to a human. A site like this would be ask.com.
The usefulness of a search engine depends on the relevance of the result set it gives back. While there may be millions of web pages that include a particular word or phrase, some pages may be more relevant, popular, or authoritative than others. Most search engines employ methods to rank the results to provide the "best" results first. How a search engine decides which pages are the best matches, and what order the results should be shown in, varies widely from one engine to another. The methods also change over time as Internet usage changes and new techniques evolve. There are two main types of search engine that have evolved: one is a system of predefined and hierarchically ordered keywords that humans have programmed extensively. The other is a system that generates an "inverted index" by analyzing texts it locates. This first form relies much more heavily on the computer itself to do the bulk of the work.
Most Web search engines are commercial ventures supported by advertising revenue and thus some of them allow advertisers to have their listings ranked higher in search results for a fee. Search engines that do not accept money for their search results make money by running search related ads alongside the regular search engine results. The search engines make money every time someone clicks on one of these ads.
The world's most popular search engines are:
|Search engine||Market share in June 2014|
East Asia and Russia
East Asian countries and Russia constitute a few places where Google is not the most popular search engine. Soso (search engine) is more popular than Google in China.
Yandex commands a marketshare of 61.9 per cent in Russia, compared to Google's 28.3 per cent. In China, Baidu is the most popular search engine. South Korea's homegrown search portal, Naver, is used for 70 per cent online searches in the country. Yahoo! Japan and Yahoo! Taiwan are the most popular avenues for internet search in Japan and Taiwan, respectively.
Search engine bias
Although search engines are programmed to rank websites based on some combination of their popularity and relevancy, empirical studies indicate various political, economic, and social biases in the information they provide. These biases can be a direct result of economic and commercial processes (e.g., companies that advertise with a search engine can become also more popular in its organic search results), and political processes (e.g., the removal of search results to comply with local laws).
Biases can also be a result of social processes, as search engine algorithms are frequently designed to exclude non-normative viewpoints in favor of more "popular" results. Indexing algorithms of major search engines skew towards coverage of U.S.-based sites, rather than websites from non-U.S. countries.
Google Bombing is one example of an attempt to manipulate search results for political, social or commercial reasons.
Customized results and filter bubbles
Many search engines such as Google and Bing provide customized results based on the user's activity history. This leads to an effect that has been called a filter bubble. The term describes a phenomenon in which websites use algorithms to selectively guess what information a user would like to see, based on information about the user (such as location, past click behaviour and search history). As a result, websites tend to show only information that agrees with the user's past viewpoint, effectively isolating the user in a bubble that tends to exclude contrary information. Prime examples are Google's personalized search results and Facebook's personalized news stream. According to Eli Pariser, who coined the term, users get less exposure to conflicting viewpoints and are isolated intellectually in their own informational bubble. Pariser related an example in which one user searched Google for "BP" and got investment news about British Petroleum while another searcher got information about the Deepwater Horizon oil spill and that the two search results pages were "strikingly different". The bubble effect may have negative implications for civic discourse, according to Pariser.
Since this problem has been identified, competing search engines have emerged that seek to avoid this problem by not tracking or "bubbling" users.
- Comparison of web search engines
- List of search engines
- Answer engine (question answering)
- Google effect
- Internet Search Engines and Libraries
- Semantic Web
- Spell checker
- Web development tools
- "World-Wide Web Servers". W3.org. Retrieved 2012-05-14.
- "What's New! February 1994". Home.mcom.com. Retrieved 2012-05-14.
- "Internet History - Search Engines" (from Search Engine Watch), Universiteit Leiden, Netherlands, September 2001, web: LeidenU-Archie.
- Oscar Nierstrasz (2 September 1993). "Searchable Catalog of WWW Resources (experimental)".
- "Archive of NCSA what's new in December 1993 page". Web.archive.org. 2001-06-20. Archived from the original on 2001-06-20. Retrieved 2012-05-14.
- "Yahoo! And Netscape Ink International Distribution Deal"
- Browser Deals Push Netscape Stock Up 7.8%. Los Angeles Times. 1 April 1996
- Gandal, Neil (2001). "The dynamics of competition in the internet search engine market". International Journal of Industrial Organization 19 (7): 1103–1117. doi:10.1016/S0167-7187(01)00065-0.
- "Our History in depth". W3.org. Retrieved 2012-10-31.
- Jawadekar, Waman S (2011), "8. Knowledge Management: Tools and Technology", Knowledge Management: Text & Cases, New Delhi: Tata McGraw-Hill Education Private Ltd, p. 278, ISBN 978-0-07-07-0086-4, retrieved November 23, 2012
- "FAQ". RankStar. Retrieved 19 June 2013.
- "Desktop Search Engine Market Share". NetMarketShare. Retrieved 2014-06-04.
- "Live Internet - Site Statistics". Live Internet. Retrieved 2014-06-04.
- Arthur, Charles (2014-06-03). "The Chinese technology companies poised to dominate the world". The Guardian. Retrieved 2014-06-04.
- "How Naver Hurts Companies’ Productivity". The Wall Street Journal. 2014-05-21. Retrieved 2014-06-04.
- "Age of Internet Empires". Oxford Internet Institute. Retrieved 2014-06-04.
- Segev, El (2010). Google and the Digital Divide: The Biases of Online Knowledge, Oxford: Chandos Publishing.
- Vaughan, Liwen; Mike Thelwall (2004). "Search engine coverage bias: evidence and possible causes". Information Processing & Management 40 (4): 693–707. doi:10.1016/S0306-4573(03)00063-3.
- Berkman Center for Internet & Society (2002), “Replacement of Google with Alternative Search Systems in China: Documentation and Screen Shots”, Harvard Law School.
- Introna, Lucas; Helen Nissenbaum (2000). "Shaping the Web: Why the Politics of Search Engines Matters". The Information Society: An International Journal 16 (3). doi:10.1080/01972240050133634.
- Parramore, Lynn (10 October 2010). "The Filter Bubble". The Atlantic. Retrieved 2011-04-20. "Since Dec. 4, 2009, Google has been personalized for everyone. So when I had two friends this spring Google "BP," one of them got a set of links that was about investment opportunities in BP. The other one got information about the oil spill...."
- Weisberg, Jacob (10 June 2011). "Bubble Trouble: Is Web personalization turning us into solipsistic twits?". Slate. Retrieved 2011-08-15.
- Gross, Doug (May 19, 2011). "What the Internet is hiding from you". CNN. Retrieved 2011-08-15. "I had friends Google BP when the oil spill was happening. These are two women who were quite similar in a lot of ways. One got a lot of results about the environmental consequences of what was happening and the spill. The other one just got investment information and nothing about the spill at all."
- Zhang, Yuan Cao; Séaghdha, Diarmuid Ó; Quercia, Daniele; Jambor, Tamas (February 2012). "Auralist: Introducing Serendipity into Music Recommendation". ACM WSDM.
- For a more detailed history of early search engines, see Search Engine Birthdays (from Search Engine Watch), Chris Sherman, September 2003.
- Steve Lawrence; C. Lee Giles (1999). "Accessibility of information on the web". Nature 400 (6740): 107–9. doi:10.1038/21987. PMID 10428673.
- Bing Liu (2007), Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer,ISBN 3-540-37881-2
- Bar-Ilan, J. (2004). The use of Web search engines in information science research. ARIST, 38, 231-288.
- Levene, Mark (2005). An Introduction to Search Engines and Web Navigation. Pearson.
- Hock, Randolph (2007). The Extreme Searcher's Handbook.ISBN 978-0-910965-76-7
- Javed Mostafa (February 2005). "Seeking Better Web Searches". Scientific American Magazine.[dead link]
- Ross, Nancy; Wolfram, Dietmar (2000). "End user searching on the Internet: An analysis of term pair topics submitted to the Excite search engine". Journal of the American Society for Information Science 51 (10): 949–958. doi:10.1002/1097-4571(2000)51:10<949::AID-ASI70>3.0.CO;2-5.
- Xie, M. et al. (1998). "Quality dimensions of Internet search engines". Journal of Information Science 24 (5): 365–372. doi:10.1177/016555159802400509.
- Information Retrieval: Implementing and Evaluating Search Engines. MIT Press. 2010.
|Wikimedia Commons has media related to Internet search engines.|