A Study of Search Engines for Health Sciences
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A Study on Vertical and Broad-Based Search Engines
International Journal of Latest Trends in Engineering and Technology IJLTET Special Issue- ICRACSC-2016 , pp.087-093 e-ISSN: 2278-621X A STUDY ON VERTICAL AND BROAD-BASED SEARCH ENGINES M.Swathi1 and M.Swetha2 Abstract-Vertical search engines or Domain-specific search engines[1][2] are becoming increasingly popular because they offer increased accuracy and extra features not possible with general, Broad-based search engines or Web-wide search engines. The paper focuses on the survey of domain specific search engine which is becoming more popular as compared to Web- Wide Search Engines as they are difficult to maintain and time consuming .It is also difficult to provide appropriate documents to represent the target data. We also listed various vertical search engines and Broad-based search engines. Index terms: Domain specific search, vertical search engines, broad based search engines. I. INTRODUCTION The Web has become a very rich source of information for almost any field, ranging from music to histories, from sports to movies, from science to culture, and many more. However, it has become increasingly difficult to search for desired information on the Web. Users are facing the problem of information overload , in which a search on a general-purpose search engine such as Google (www.google.com) results in thousands of hits.Because a user cannot specify a search domain (e.g. medicine, music), a search query may bring up Web pages both within and outside the desired domain. Example 1: A user searching for “cancer” may get Web pages related to the disease as well as those related to the Zodiac sign. -
A Meta Search Engine Based on a New Result Merging Strategy
Usearch: A Meta Search Engine based on a New Result Merging Strategy Tarek Alloui1, Imane Boussebough2, Allaoua Chaoui1, Ahmed Zakaria Nouar3 and Mohamed Chaouki Chettah3 1MISC Laboratory, Department of Computer Science and its Applications, Faculty of NTIC, University Constantine 2 Abdelhamid Mehri, Constantine, Algeria 2LIRE Laboratory, Department of Software Technology and Information Systems, Faculty of NTIC, University Constantine 2 Abdelhamid Mehri, Constantine, Algeria 3Department of Computer Science and its Applications, Faculty of NTIC, University Constantine 2, Abdelhamid Mehri, Constantine, Algeria Keywords: Meta Search Engine, Ranking, Merging, Score Function, Web Information Retrieval. Abstract: Meta Search Engines are finding tools developed for improving the search performance by submitting user queries to multiple search engines and combining the different search results in a unified ranked list. The effectiveness of a Meta search engine is closely related to the result merging strategy it employs. But nowadays, the main issue in the conception of such systems is the merging strategy of the returned results. With only the user query as relevant information about his information needs, it’s hard to use it to find the best ranking of the merged results. We present in this paper a new strategy of merging multiple search engine results using only the user query as a relevance criterion. We propose a new score function combining the similarity between user query and retrieved results and the users’ satisfaction toward used search engines. The proposed Meta search engine can be used for merging search results of any set of search engines. 1 INTRODUCTION advantages of MSEs are their abilities to combine the coverage of multiple search engines and to reach Nowadays, the World Wide Web is considered as the deep Web. -
Meta Search Engine with an Intelligent Interface for Information Retrieval on Multiple Domains
International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol.1, No.4, October 2011 META SEARCH ENGINE WITH AN INTELLIGENT INTERFACE FOR INFORMATION RETRIEVAL ON MULTIPLE DOMAINS D.Minnie1, S.Srinivasan2 1Department of Computer Science, Madras Christian College, Chennai, India [email protected] 2Department of Computer Science and Engineering, Anna University of Technology Madurai, Madurai, India [email protected] ABSTRACT This paper analyses the features of Web Search Engines, Vertical Search Engines, Meta Search Engines, and proposes a Meta Search Engine for searching and retrieving documents on Multiple Domains in the World Wide Web (WWW). A web search engine searches for information in WWW. A Vertical Search provides the user with results for queries on that domain. Meta Search Engines send the user’s search queries to various search engines and combine the search results. This paper introduces intelligent user interfaces for selecting domain, category and search engines for the proposed Multi-Domain Meta Search Engine. An intelligent User Interface is also designed to get the user query and to send it to appropriate search engines. Few algorithms are designed to combine results from various search engines and also to display the results. KEYWORDS Information Retrieval, Web Search Engine, Vertical Search Engine, Meta Search Engine. 1. INTRODUCTION AND RELATED WORK WWW is a huge repository of information. The complexity of accessing the web data has increased tremendously over the years. There is a need for efficient searching techniques to extract appropriate information from the web, as the users require correct and complex information from the web. A Web Search Engine is a search engine designed to search WWW for information about given search query and returns links to various documents in which the search query’s key words are found. -
Meta Search Engine Examples
Meta Search Engine Examples mottlesMarlon istemerariously unresolvable or and unhitches rice ichnographically left. Salted Verney while crowedanticipated no gawk Horst succors underfeeding whitherward and naphthalising. after Jeremy Chappedredetermines and acaudalfestively, Niels quite often sincipital. globed some Schema conflict can be taken the meta descriptions appear after which result, it later one or can support. Would result for updating systematic reviews from different business view all fields need to our generated usually negotiate the roi. What is hacking or hacked content? This meta engines! Search Engines allow us to filter the tons of information available put the internet and get the bid accurate results And got most people don't. Best Meta Search array List The Windows Club. Search engines have any category, google a great for a suggestion selection has been shown in executive search input from health. Search engine name of their booking on either class, the sites can select a search and generally, meaning they have past the systematisation of. Search Engines Corner Meta-search Engines Ariadne. Obsession of search engines such as expedia, it combines the example, like the answer about search engines out there were looking for. Test Embedded Software IC Design Intellectual Property. Using Research Tools Web Searching OCLS. The meta description for each browser settings to bing, boolean logic always prevent them the hierarchy does it displays the search engine examples osubject directories. Online travel agent Bookingcom has admitted that playing has trouble to compensate customers whose personal details have been stolen Guests booking hotel rooms have unwittingly handed over business to criminals Bookingcom is go of the biggest online travel agents. -
Google Overview Created by Phil Wane
Google Overview Created by Phil Wane PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Tue, 30 Nov 2010 15:03:55 UTC Contents Articles Google 1 Criticism of Google 20 AdWords 33 AdSense 39 List of Google products 44 Blogger (service) 60 Google Earth 64 YouTube 85 Web search engine 99 User:Moonglum/ITEC30011 105 References Article Sources and Contributors 106 Image Sources, Licenses and Contributors 112 Article Licenses License 114 Google 1 Google [1] [2] Type Public (NASDAQ: GOOG , FWB: GGQ1 ) Industry Internet, Computer software [3] [4] Founded Menlo Park, California (September 4, 1998) Founder(s) Sergey M. Brin Lawrence E. Page Headquarters 1600 Amphitheatre Parkway, Mountain View, California, United States Area served Worldwide Key people Eric E. Schmidt (Chairman & CEO) Sergey M. Brin (Technology President) Lawrence E. Page (Products President) Products See list of Google products. [5] [6] Revenue US$23.651 billion (2009) [5] [6] Operating income US$8.312 billion (2009) [5] [6] Profit US$6.520 billion (2009) [5] [6] Total assets US$40.497 billion (2009) [6] Total equity US$36.004 billion (2009) [7] Employees 23,331 (2010) Subsidiaries YouTube, DoubleClick, On2 Technologies, GrandCentral, Picnik, Aardvark, AdMob [8] Website Google.com Google Inc. is a multinational public corporation invested in Internet search, cloud computing, and advertising technologies. Google hosts and develops a number of Internet-based services and products,[9] and generates profit primarily from advertising through its AdWords program.[5] [10] The company was founded by Larry Page and Sergey Brin, often dubbed the "Google Guys",[11] [12] [13] while the two were attending Stanford University as Ph.D. -
DEEP WEB IOTA Report for Tax Administrations
DEEP IOTA Report for Tax Administrations IOTA Report for Tax Administrations – Deep Web DEEP WEB IOTA Report for Tax Administrations Intra-European Organisation of Tax Administrations (IOTA) Budapest 2012 1 IOTA Report for Tax Administrations – Deep Web PREFACE This report on deep Web investigation is the second report from the IOTA “E- Commerce” Task Team of the “Prevention and Detection of VAT Fraud” Area Group. The team started operations in January 2011 in Wroclaw, Poland initially focusing its activities on problems associated with the audit of cloud computing, the report on which was published earlier in 2012. During the Task Teams’ second phase of work the focus has been on deep Web investigation. What can a tax administration possibly gain from the deep Web? For many the deep Web is something of a mystery, something for computer specialists, something they have heard about but do not understand. However, the depth of the Web should not represent a threat as the deep Web offers a very important source of valuable information to tax administrations. If you want to understand, to master and to fully exploit the deep Web, you need to see the opportunities that arise from using information buried deep within the Web, how to work within the environment and what other tax administrations have already achieved. This report is all about understanding, mastering and using the deep Web as the key to virtually all audits, not just those involving E-commerce. It shows what a tax administration can achieve by understanding the deep Web and how to use it to their advantage in every audit. -
List of Search Engines
A blog network is a group of blogs that are connected to each other in a network. A blog network can either be a group of loosely connected blogs, or a group of blogs that are owned by the same company. The purpose of such a network is usually to promote the other blogs in the same network and therefore increase the advertising revenue generated from online advertising on the blogs.[1] List of search engines From Wikipedia, the free encyclopedia For knowing popular web search engines see, see Most popular Internet search engines. This is a list of search engines, including web search engines, selection-based search engines, metasearch engines, desktop search tools, and web portals and vertical market websites that have a search facility for online databases. Contents 1 By content/topic o 1.1 General o 1.2 P2P search engines o 1.3 Metasearch engines o 1.4 Geographically limited scope o 1.5 Semantic o 1.6 Accountancy o 1.7 Business o 1.8 Computers o 1.9 Enterprise o 1.10 Fashion o 1.11 Food/Recipes o 1.12 Genealogy o 1.13 Mobile/Handheld o 1.14 Job o 1.15 Legal o 1.16 Medical o 1.17 News o 1.18 People o 1.19 Real estate / property o 1.20 Television o 1.21 Video Games 2 By information type o 2.1 Forum o 2.2 Blog o 2.3 Multimedia o 2.4 Source code o 2.5 BitTorrent o 2.6 Email o 2.7 Maps o 2.8 Price o 2.9 Question and answer . -
Architecture Based Study of Search Engines and Meta Search Engines for Information Retrieval A
International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 2 Issue 5, May - 2013 Architecture Based Study Of Search Engines And Meta Search Engines For Information Retrieval A. Madhavi1,K. Harisha Chari2 1Asst.Professor, Matrusri Institute of PG Studies, Hyderabad, India. 2Asst.Professor, K.V Ranga Reddy College, Hyderabad, India. Abstract not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to WWW is a huge repository of information. republish to post on servers or to redistribute to The complexity of accessing the web data has lists, requires prior specific permission and/or a fee. increased tremendously over the years. There is Single search engine are increased coverage and a a need for efficient searching techniques to consistent interface. A recent study by Lawrence extract appropriate information from the web, as and Giles estimated the size of the web at about 800 million indexable pages. This same study the users require correct and complex concluded that no single search engine covered information from the web. more than about sixteen percent of the total. By This paper analyses the architectures and searching multiple search engines simultaneously features of metasearch engines for searching and via a metasearch engine, coverage increases receiving documents on single and multiples dramatically over searching only one engine. domains on the web. A web search engine searches Lawrence and Giles found that combining the for information in WWW. results of 11 major search engines increased the coverage to about 42% of the estimated size of the 1. -
Harnessing the Deep Web: Present and Future
Harnessing the Deep Web: Present and Future Jayant Madhavan Loredana Afanasiev Lyublena Antova Alon Halevy Google Inc. Universiteit van Amsterdam Cornell University Google Inc. [email protected] [email protected] [email protected] [email protected] 1. INTRODUCTION pre-compute queries to forms and inserts the resulting pages The Deep Web refers to content hidden behind HTML into a web-search index. These pages are then treated like forms. In order to get to such content, a user has to perform any other page in the index and appear in answers to web- a form submission with valid input values. The name Deep search queries. We have pursued both approaches in our Web arises from the fact that such content was thought to work. In Section 3 we explain our experience with both, be beyond the reach of search engines. The Deep Web is and where each approach provides value. also believed to be the biggest source of structured data on We argue that the value of the virtual integration ap- the Web and hence accessing its contents has been a long proach is in constructing vertical search engines in specific standing challenge in the data management community [1, domains. It is especially useful when it is not enough to 8, 9, 13, 14, 18, 19]. just focus on retrieving data from the underlying sources, Over the past few years, we have built a system that ex- but when users expect a deeper experience with the con- posed content from the Deep Web to web-search users of tent (e.g., making purchases) after they found what they Google.com. -
CS 101 - Intro to Computer Science Such Software Has Capabilities Such As: Search Engines Organizing an Index to the Information in a Database
What is a Search Engine? A search engine is a computer program that allows a user to submit a query and retrieve information from a database. CS 101 - Intro to Computer Science Such software has capabilities such as: Search Engines organizing an index to the information in a database, Dr. Stephen P. Carl enabling the formulation of a query by a user, and searching the index in response to a query. Web Crawlers - Mining Web Information Types of web search engines A search engine for the WWW uses a program called a robot or spider to index the information on Web pages. Topical search engines organize their catalogs of sites by topic or subject. Examples are Yahoo! and Lycos a2z. Spiders work by following all the links on a page according to a specific search strategy. The simplest strategy is to collect all links from a page and then follow them, one by one, collecting new links as new pages are visited. The content of each page is added to a database. The database is indexed and this index is searched upon receipt of a query from the user; the search engine then presents a sorted list of matching results to the user. 1-4 Types of web search engines Types of web search engines Keyword or Key Phrase search engines let the user specify a set of Metasearch Engines send queries to several other search engines keywords or phrases related to the desired content. and consolidate the results. Some, such as ProFusion, filter the results to remove duplicates and check the validity of the links. -
Search Engines and Power: a Politics of Online (Mis-) Information
5/2/2020 Search Engines and Power: A Politics of Online (Mis-) Information Webology, Volume 5, Number 2, June, 2008 Table of Titles & Subject Authors Home Contents Index Index Search Engines and Power: A Politics of Online (Mis-) Information Elad Segev Research Institute for Law, Politics and Justice, Keele University, UK Email: e.segev (at) keele.ac.uk Received March 18, 2008; Accepted June 25, 2008 Abstract Media and communications have always been employed by dominant actors and played a crucial role in framing our knowledge and constructing certain orders. This paper examines the politics of search engines, suggesting that they increasingly become "authoritative" and popular information agents used by individuals, groups and governments to attain their position and shape the information order. Following the short evolution of search engines from small companies to global media corporations that commodify online information and control advertising spaces, this study brings attention to some of their important political, social, cultural and economic implications. This is indicated through their expanding operation and control over private and public informational spaces as well as through the structural bias of the information they attempt to organize. In particular, it is indicated that search engines are highly biased toward commercial and popular US- based content, supporting US-centric priorities and agendas. Consequently, it is suggested that together with their important role in "organizing the world's information" search engines -
An Intelligent Metasearch Engine for the World Wide
AN INTELLIGENTMETASEARCH ENGINE FOR THE WORLDWIDE WEB Andrew Agno A thesis submitted in conformity with the requirements for the degree of Master of Science Graduate Department of Cornputer Science University of Toronto Copyright @ 2000 by Andrew Agno National Library Bibliothèque nationale ($1 of Canada du Canada Acquisitions and Acquisitions et Bibfiographic Services services bibliographiques 395 Wellington Street 395. rue Wellington OttawaON K1AON4 Ottawa ON K1A ON4 Canada Canada The author has granted a non- L'auteur a accordé une licence non exclusive licence allowing the exclusive permettant à la National Lïbrary of Canada to Bibliothèque nationale du Canada de reproduce, loan, distribute or sel1 reproduire, prêter, distribuer ou copies of this thesis in rnicrofonn, vendre des copies de cette thèse sous paper or electronic formats. la forme de microfiche/film, de reproduction sur papier ou sur format électronique. The author retains ownership of the L'auteur conserve la propriété du copyright in this thesis. Neither the droit d'auteur qui protège cette thèse. thesis nor substantial extracts ffom it Ni la thèse ni des extraits substantiels may be p~tedor otherwise de celle-ci ne doivent être imprimés reproduced without the author's ou autrement reproduits sans son permission. autorisation. tract An Intelligent Met asearch Engine for the n'orld nïde Uéb .Anchen- Agno Mas ter of Science Graduate Department of Cornputer Science Cniversi ty of Toronto 2000 Uachine learning and informat ion retried techniques are appliecl t o met asearch on the Vorld n'ide ?\éb as a means of providing user specific relennr documents in respome to user queries.