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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. -
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. -
DMU Handout for Presentations 3 5 12
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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. -
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. -
Developing a Cybervetting Strategy for Law Enforcement
Developing a Cybervetting Strategy for Law EnforcEmEnt Defense Personnel Security Research Center U.S. Department of Defense DEVELOPING A CYBERVETTING STRATEGY FOR LAW ENFORCEMENT SPECIAL REPORT Andrée Rose Howard Timm Corrie Pogson Jose Gonzalez Edward Appel Nancy Kolb December 2010 CONTENTS Section 1. Introduction � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 1 Background � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 1 Approach � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 2 How to Use This Document � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 3 Section 2. Developing a Cybervetting Policy � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 4 Purpose and Scope � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 4 Preemployment Screening and Post Hire Monitoring � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � -
List of Search Engines Listed by Types of Searches
List of Search Engines Listed by Types of Searches - © 09-14-2014 - images removed Copyright - Professional Web Services Internet Marketing SEO Business Solutions - http://pwebs.net - All Rights Reserved List of Search Engines Listed by Types of Searches by ProWebs - Article last updated on: Saturday, July 28, 2012 http://pwebs.net/2011/04/search-engines-list/ To view images click link above. This page posting has been updated with the addition of some of the newer search engines. The original search engine page was dealing mainly with B2B and B2C search. However, with today's broad based Internet activity, and how it relates to various aspects of business usage, personal searches, and even internal intranet enterprise searches, we wanted to highlight some of the different search market segments. This list of the various search engines, is posted here mainly for research and education purposes. We felt it is nice to be able to reference and have links to some of the different search engines all in one place. While the list below does not cover each and every search engine online, it does provide a broad list of most of the major search companies that are available. Also note that some of the search engine links have been redirected to other sites due to search buyouts, mergers, and acquisitions with other companies, along with changes with the search engines themselves (for example: search companies have changed brand names, different URLs, and links). You will also notice that some of the search websites go back a number of years. If you would like to read more about the details of any particular search engine, take a look at the Wikipedia Search Engines List article, and follow the links to each specific search engine for a descriptive overview. -
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 Meta Search Engine for Efficient Web Document Retrieval
IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 2, Ver. V (Mar – Apr. 2015), PP 45-54 www.iosrjournals.org An Intelligent Meta Search Engine for Efficient Web Document Retrieval 1 2 Keerthana.I.P , Aby Abahai.T 1(Dept. of CSE, Mar Athanasius College of Engineering, Kothamangalam , Kerala, India) 2(Dept. of CSE, Assistant Professor, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India) Abstract: In daily use of internet, when searching information we face lots of difficulty due to the rapid growth of Information Resources. This is because of the fact that a single search engine cannot index the entire web of resources. A Meta Search Engine is a solution for this, which submits the query to many other search engines and returns summary of the results. Therefore, the search results receive are an aggregate result of multiple searches. This strategy gives our search a boarder scope than searching a single search engine, but the results are not always better. Because the Meta Search Engine must use its own algorithm to choose the best results from multiple search engines. In this paper we proposed a new Meta Search Engine to overcome these drawbacks. It uses a new page rank algorithm called modified ranking for ranking and optimizing the search results in an efficient way. It is a two phase ranking algorithm used for ordering the web pages based on their relevance and popularity. This Meta Search Engine is developed in such a way that it will produce more efficient results than traditional search engines. -
Introduction to Web Search Engines
Journal of Novel Applied Sciences Available online at www.jnasci.org ©2014 JNAS Journal-2014-3-7/724-728 ISSN 2322-5149 ©2014 JNAS INTRODUCTION TO WEB SEARCH ENGINES Mohammad Reza pourmir Computer Engineering Department, Faculty of Engineering, University of zabol, Zabol, Iran Corresponding author: Mohammad Reza Pourmir ABSTRACTA: web search engine is a program designed to help find information stored on the World Wide Web. Search engine indexes are similar, but vastly more complex that back of the book indexes. The quality of the indexes, and how the engines use the information they contain, is what makes or breaks the quality of search results. The vast majority of users navigate the Web via search engines. Yet searching can be the most frustrating activity using a browser. Type in a keyword or phrase, and we're likely to get thousands of responses, only a handful of which are close to what we looking for. And those are located on secondary search pages, only after a set of sponsored links or paid-for-position advertisements. Still, search engines have come a long way in the past few years. Although most of us will never want to become experts on web indexing, knowing even a little bit about how they're built and used can vastly improve our searching skills. This paper gives a brief introduction to the web search engines, architecture, the work process, challenges faced by search engines, discuss various searching strategies, and the recent technologies in the web mining field. Keywords: Introduction, Web Search, Engines. INTRODUCTION A web search engine is a program designed to help find information stored on the World Wide Web. -
LIVIVO – the Vertical Search Engine for Life Sciences
Datenbank Spektrum DOI 10.1007/s13222-016-0245-2 SCHWERPUNKTBEITRAG LIVIVO – the Vertical Search Engine for Life Sciences Bernd Müller1 · Christoph Poley1 · Jana Pössel1 · Alexandra Hagelstein1 · Thomas Gübitz1 Received: 14 October 2016 / Accepted: 23 December 2016 © The Author(s) 2017. This article is available at SpringerLink with Open Access. Abstract The explosive growth of literature and data in the Future work will focus on the exploitation of life science life sciences challenges researchers to keep track of current ontologies and on the employment of NLP technologies in advancements in their disciplines. Novel approaches in the order to improve query expansion, filters in faceted search, life science like the One Health paradigm require integrated and concept based relevancy rankings in LIVIVO. methodologies in order to link and connect heterogeneous information from databases and literature resources. Cur- Keywords Literature Search · Knowledge Discovery · rent publications in the life sciences are increasingly char- LIVIVO · Life Sciences · Information Retrieval · Text acterized by the employment of trans-disciplinary method- Mining · Data Mining ologies comprising molecular and cell biology, genetics, genomic, epigenomic, transcriptional and proteomic high throughput technologies with data from humans, plants, 1 Interdisciplinary Research and Vertical and animals. The literature search engine LIVIVO empow- Information Search ers retrieval functionality by incorporating various literature resources from medicine, health, environment, agriculture 1.1 Why We Need Interdisciplinary Research and and nutrition. LIVIVO is developed in-house by ZB MED – Development Information Centre for Life Sciences. It provides a user- friendly and usability-tested search interface with a corpus Diseases and their treatment are one of the greatest chal- of 55 Million citations derived from 50 databases. -
Web Crawling with Carlos Castillo
Modern Information Retrieval Chapter 12 Web Crawling with Carlos Castillo Applications of a Web Crawler Architecture and Implementation Scheduling Algorithms Crawling Evaluation Extensions Examples of Web Crawlers Trends and Research Issues Web Crawling, Modern Information Retrieval, Addison Wesley, 2010 – p. 1 Introduction and a Brief History A Web Crawler is a software for downloading pages from the Web Also known as Web Spider, Web Robot, or simply Bot Cycle of a Web crawling process The crawler start downloading a set of seed pages, that are parsed and scanned for new links The links to pages that have not yet been downloaded are added to a central queue for download later Next, the crawler selects a new page for download and the process is repeated until a stop criterion is met Web Crawling, Modern Information Retrieval, Addison Wesley, 2010 – p. 2 Introduction and a Brief History The first known web crawler was created in 1993 by Matthew Gray, an undergraduate student at MIT On June of that year, Gray sent the following message to the www-talk mailing list: “I have written a perl script that wanders the WWW collecting URLs, keeping tracking of where it’s been and new hosts that it finds. Eventually, after hacking up the code to return some slightly more useful information (currently it just returns URLs), I will produce a searchable index of this.” The project of this Web crawler was called WWWW (World Wide Web Wanderer) It was used mostly for Web characterization studies Web Crawling, Modern Information Retrieval, Addison Wesley, 2010 – p. 3 Introduction and a Brief History On June 1994, Brian Pinkerton, a PhD student at the University of Washington, posted the following message to the comp.infosystems.announce news “The WebCrawler Index is now available for searching! The index is broad: it contains information from as many different servers as possible.