Metasearch engine

Architecture of a metasearch engine

A metasearch engine (or aggregator) is a search tool that uses another search engine's data to produce their own results from the Internet.[1][2] Metasearch engines take input from a user and simultaneously send out queries to third party search engines for results. Sufficient data is gathered, formatted by their ranks and presented to the users.

However, Metasearch also has issues. Scores of websites stored on search engines are all different: this can draw in irrelevant documents. Other problems such as spamming also significantly reduce the accuracy of the search.[3] The process of fusion aims to tackle this issue and improve the engineering of a metasearch engine.[4]

There are many types of metasearch engines available to allow users to access specialised information in a particular field. These include Savvysearch engine and Metaseek engine.


"Why search the web with one search engine when you can search them all - or at least several?" This was the question tackled by researchers following a search engine review that found different search engines to be producing different results because of the different algorithms on which each was based.

The first person to incorporate the idea of meta searching was Colorado State University's Daniel Dreilinger. He revealed SearchSavvy, which let users search up to 20 different search engines and directories at once. Although fast, the search engine was restricted to simple searches and thus wasn't too reliable. University of Washington student Eric Selberg released a more "updated" version called MetaCrawler. This search engine improved on SearchSavvy's accuracy by adding its own search syntax behind the scenes, and matching the syntax to that of the search engines it was probing. Metacrawler reduced the amount of search engines queried to 6, but although it produced more accurate results, it still wasn't considered as accurate as searching a query in an individual engine.[5]

Another metasearch engine was created in May 20, 1996. HotBot, owned by Wired at the time, was a search engine with search results coming from the Inktomi and Direct Hit database. It was known at the time for its fast results and funky name, and as a search engine with the ability to search within search results. Upon being bought by Lycos in 1998, development for the search engine staggered and its market share fell drastically. After going through a few alterations, HotBot was redesigned into a simplified search interface, with its features being incorporated into Lycos' website redesign.[6]

Ixquick is a search engine more recently known for its privacy policy statement. Developed and launched in 1998 by David Bodnick, it is currently owned by Surfboard Holding BV as of year 2000. On June 2006, Ixquick began to delete private details of its users following the same process with Scroogle. Ixquick's privacy policy includes no recording of users' IP addresses, no identifying cookies, no collection of personal data, and no sharing of personal data with third parties.[7] It also uses a unique ranking system where a result is ranked by stars. The more stars in a result, the more search engines agreed on the result.

In April 2005, Dogpile (owned and operated by InfoSpace, Inc. at the time) collaborated with researchers from University of Pittsburgh and Pennsylvania State University to measure the overlap and ranking differences of leading Web search engines in order to gauge the benefits of using a metasearch engine to search the web. Results found that from 10,316 random user-defined queries from Google, Yahoo!, and Ask Jeeves, only 3.2 percent of first page search results were the same across those search engines for a given query. Another study later that year using 12,570 random user-defined queries from Google, Yahoo!, MSN Search, and Ask Jeeves found that only 1.1 percent of first page search results were the same across those search engines for a given query.


By sending multiple queries to several other search engines this extends the search coverage of the topic and allows more information to be found. They use the indexes built by other search engines, aggregating and often post-processing results in unique ways. A metasearch engine has an advantage over a single search engine because more results can be retrieved with the same amount of exertion.[2] It also reduces the work of users from having to individually type in searches from different engines to look for resources.[2]

Metasearching is also a useful approach if the purpose of the user’s search is to get an overview of the topic or to get quick answers. Instead of having to go through multiple search engines like Yahoo! or Google and comparing results, metasearch engines are able to quickly compile and combine results. They can do it either by listing results from each engine queried with no additional post-processing (Dogpile) or by analyzing the results and ranking them by their own rules (IxQuick, Metacrawler, and Vivismo).[8]


Metasearch engines are not capable of decoding query forms or able to fully translate query syntax. The number of links generated by metasearch engines are limited, and therefore do not provide the user with the complete results of a query.[9] The majority of metasearch engines do not provide over ten linked files from a single search engine, and generally do not interact with larger search engines for results. Sponsored webpages are prioritised and are normally displayed first.[10]

Metasearching also gives the illusion that there is more coverage of the topic queried, particularly if the user is searching for popular or commonplace information. It's common to end with multiple identical results from the queried engines. It is also harder for users to search with advanced search syntax to be sent with the query, so results may not be as precise as when a user is using an advanced search interface at a specific engine. This results in many metasearch engines using simple searching.[11]


A metasearch engine accepts a single search request from the user. This search request is then passed on to another search engine’s database. A metasearch engine does not create a database of webpages but generates a virtual database to integrate data from multiple sources.[12][13][14]

Since every search engine is unique and has different algorithms for generating ranked data, duplicates will therefore also be generated. To remove duplicates,a metasearch engine processes this data and applies its own algorithm. A revised list is produced as an output for the user.[15][16] When a metasearch engine contacts other search engines, these search engines will respond in three ways:

Architecture of ranking

Webpages that are highly ranked on many search engines are likely to be more relevant in providing useful information.[17] However, all search engines have different ranking scores for each website and most of the time these scores are not the same. This is because search engines prioritise different criteria and methods for scoring, hence a website might appear highly ranked on one search engine and lowly ranked on another. This is a problem because Metasearch engines rely heavily on the consistency of this data to generate reliable accounts.[17]


Data Fusion Model

A metasearch engine uses the process of Fusion to filter data for more efficient results. The two main fusion methods used are: Collection Fusion and Data Fusion.


Spamdexing is the deliberate manipulation of search engine indexes. It uses a number of methods to manipulate the relevance or prominence of resources indexed in a manner unaligned with the intention of the indexing system. Spamdexing can be very distressing for users and problematic for search engines because the return contents of searches have poor precision.[20] This will eventually result in the search engine becoming unreliable and not dependable for the user. To tackle Spamdexing, search robot algorithms are made more complex and are changed almost everyday to eliminate the problem.[21]

It is a major problem for metasearch engines because it tampers with the search robot’s indexing criteria, which are heavily relied upon to format ranking lists. Spamdexing manipulates the natural ranking system of a search engine, and places websites higher on the ranking list than they would naturally be placed.[22] There are three primary methods used to achieve this:

Content spam

Content spam are the techniques that alter the logical view that a search engine has over the page's contents. Techniques include:

Link spam

Link spam are links between pages present for reasons other than merit. Techniques include:


This is a SEO technique in which different materials and information are sent to the web crawler and to the web browser.[23] It is commonly used as a spamdexing technique because it can trick search engines into either visiting a site that is substantially different from the search engine description or giving a certain site a higher ranking.

See also


  1. Sandy Berger's Great Age Guide to the Internet by Sandy Berger. Que Publishing, 2005, ISBN 0-7897-3442-7.
  2. 1 2 3 "Architecture of a Metasearch Engine that Supports User Information Needs" (PDF). Proceedings of the Eighth International Conference on Information Knowledge Management. 1999.
  3. Lawrence, S. and Lee Giles, C. (2014). Patent US6999959 - Meta search engine. [online] Google Books. Available at: [Accessed 20 Oct. 2014].
  4. The collection fusion problem. By E. M. Voorhees, Narendra K. Gupta, and Ben Johnson-Laird. NIST SPECIAL PUBLICATION SP, 1995, 95-95.
  5. "The Meta-search -- Search Engine History". Retrieved 5 August 2015.
  6. "Search engine rankings on HotBot: a brief history of the HotBot search engine". Retrieved 5 August 2015.
  7. "ABOUT US - Our history". Retrieved 5 August 2015.
  8. Chris Sherman. "Meta Search Engine Week!". Search Engine Watch. Retrieved 5 August 2015.
  9. "Welcome to the Department of Informatics at the University of Fribourg". Retrieved 5 August 2015.
  10., (2014). 42. [online] Available at: http://Intelligence Exploitation of the internet [Accessed 31 Oct. 2014].
  12. MENG, W. (2014). Metasearch Engines. [online] Available at: [Accessed 20 Oct. 2014].
  13. Selberg, E., Etzioni ,O. (1997). "The MetaCrawler architecture for resource aggregation on the Web.". IEEE expert. pp. 11–14. Retrieved April 3, 2014.
  14. Manoj, M Jacob, E. (July 2013). "Design and Development of a Programmable Meta Search Engine" (PDF). Foundation of Computer Science. pp. 6–11. Retrieved April 3, 2014.
  15. Patel, B. and Shah, D. (2014). RANKING ALGORITHM FOR META SEARCH ENGINE. [online] Available at: [Accessed 21 Oct. 2014]..
  16. Manoj, M Elizabeth, Jacob (October 2008). "Information retrieval on Internet using Metasearch engines: A review". CSIR. pp. 739–746. Retrieved February 25, 2012.
  17. 1 2 3 4 M, M. and Jacob, E. (2014). Information retrieval on Internet using meta-search engines: A review. [online] Available at: [Accessed 27 Oct. 2014].
  18. Wu, S.; Crestani, F.; Bi, Y. (2006). "Evaluating Score Normalization Methods in Data Fusion". Information Retrieval Technology: 642–648. doi:10.1007/11880592_57.
  19. Manmatha, R. and Sever, H. (2014). A Formal Approach to Score Normalization for Meta-search. [online] Available at: [Accessed 27 Oct. 2014].
  20. Al-Kabi, M., Wahsheh, H. and Alsmadi, I. (2014). An Online Arabic Web Spam Detection System. [online] Available at: [Accessed 24 Oct. 2014].
  21. Najork, M. (2014). Web Spam Detection. [online] Available at: [Accessed 23 Oct. 2014].
  22. Vandendriessche, G., Altius, and Brussels, (2014). A few legal comments on spamdexing. [online] Available at: [Accessed 22 Oct. 2014].
  23. Wang, Y., Ma, M., Niu, Y. and Chen, H. (2014). Connecting Web Spammers with Advertisers. [online] Available at: [Accessed 24 Oct. 2014].
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