DOCSLIB.ORG

Improving Search Engines Via Classification

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Improving Search Engines via Classi¯cation Zheng Zhu May 2011 A Dissertation Submitted to Birkbeck College, University of London in Partial Ful¯llment of the Requirements for the Degree of Doctor of Philosophy Department of Computer Science & Information Systems Birkbeck College University of London Declaration This thesis is the result of my own work, except where explicitly acknowledge in the text. Zheng Zhu i Abstract In this dissertation, we study the problem of how search engines can be improved by making use of classi¯cation. Given a user query, traditional search engines output a list of results that are ranked according to their relevance to the query. However, the ranking is independent of the topic of the document. So the results of di®erent topics are not grouped together within the result output from a search engine. This can be problematic as the user must scroll though many irrelevant results until his/her desired information need is found. This might arise when the user is a novice or has super¯cial knowledge about the domain of interest, but more typically it is due to the query being short and ambiguous. One solution is to organise search results via categorization, in particular, the classi¯cation. We designed a target testing experiment on a controlled data set, which showed that classi¯cation-based search could improve the user's search experience in terms of the numbers of results the user would have to inspect before satisfying his/her query. In our investigation of classi¯cation to organise search results, we not only consider the classi¯cation of search results, but also query classi¯cation. In particular, we investigate the case where the enrichment of the training and test queries is asymmetrical. We also make use of a large search engine log to provide a comprehensive topic speci¯c analysis of search engine queries. Finally we study the problem of ranking the classes using some new features derived from the class. The contribution of this thesis is the investigation of classi¯cation-based search in terms of ranking the search results. This allows us to analyze the ii e®ect of a classi¯er's performance on a classi¯cation-based search engines along with di®erent user interaction models. Moreover, it allows the evaluation of class-based ranking methods. iii Publications Publications Relating to the thesis 1. Zheng Zhu, Ingemar J Cox, Mark Levene: \Ranked-Listed or Cate- gorized Results in IR: 2 Is Better Than 1". The 13th international conference on Natural Language and Information Systems, 2008, pages 111-123, London, United Kingdom - Chapter 3 2. Judit Bar-Ilan, Zheng Zhu and Mark Levene: \Topic-speci¯c analysis of search queries". The 2009 workshop on Web Search Click Data (WSCD '09), pages 35-42, Barcelona, Spain Extended version appeared as Judit Bar-Ilan, Zheng Zhu and Mark Levene: \Topical Analysis of Search Queries ". in the 10th Bar-Ilan Symposium on the Foundations of Arti¯cial Intelligence - Chapter 4 3. Zheng Zhu, Mark Levene, Ingemar J Cox: \Query classi¯cation us- ing asymmetric learning ". The 2nd International Conference on the Applications of Digital Information and Web Technologies, 2009, pages 518-524, London, Unite Kingdom - Chapter 4 4. Zheng Zhu, Mark Levene, Ingemar J Cox: \Ranking Classes of Search Engine Results". The International Conference on Knowledge Discov- ery and Information Retrieval, 2010, Valencia, Spain - Chapter 5 iv Acknowledgements I am deeply grateful to my supervisors, Prof. Mark Levene and Prof. Inge- mar J Cox, for their continued support, their patient guidance and their faith in my work throughout these years. Whenever I needed any help, their doors were always open. Many thanks are due to my colleagues at Birkbeck, University of London. Without their collaboration and discussions, I can not have ¯nished our research work. I would particularly like to thank Dell Zhang, Rajesh Pampapathi, Phil Gregg, Aisling Traynor and Simon Upton for their help and support. Dr. Iadh Ounis and Dr. Tassos Tombros gave me critical comments during the defense, which will make me to think a lot more deeply about my work from here on in. Special thanks are due to Dr. Kai Yu and Sean Zhang. Their encouragement and suggestions guided me towards starting my Ph.D in the very beginning. I would also thank Martyn Harris for the ¯nal proofreading. Last but not least,I would like to declare my gratitude and love to my wife Lei Zhu and my parents YongGang Zhu, XiangYa Zheng. Their enthusiasm and constant encouragement with their patience accompany me through this period and every other period. I would never have ¯nished this work without their support. v Contents 1 Introduction 1 1.1 Current State of the Art in Search ................. 1 1.2 What is Lacking in Search ...................... 4 1.3 Classi¯cation as a Way to Organise Search Results ........ 5 1.4 Outline of the Thesis ......................... 7 2 Background 10 2.1 Web Information Retrieval ...................... 10 2.1.1 Retrieval Models ....................... 14 2.1.2 Evaluation Metric ...................... 17 2.1.3 Text Analysis Process .................... 21 2.2 Machine Learning ........................... 22 2.2.1 Unsupervised Learning .................... 24 2.2.2 Supervised Learning ..................... 25 2.3 Web Content Clustering and Classi¯cation ............. 31 2.3.1 Web Content Clustering ................... 31 2.3.2 Web Content Classi¯cation ................. 32 2.3.3 Search Results Classi¯cation ................ 34 vi 3 A Model for Classi¯cation-Based Search Engines 40 3.1 Problem Description ......................... 40 3.2 Classi¯cation-Based Search Engines ................ 41 3.2.1 Class Rank .......................... 43 3.2.2 Document Rank ....................... 44 3.2.3 Scrolled-Class Rank (SCR) ................. 45 3.2.4 In-Class Rank (ICR) ..................... 45 3.2.5 Out-Class/Scrolled-Class Rank (OSCR) and Out-Class/Revert Rank (ORR) ......................... 46 3.2.6 Classi¯cation ......................... 47 3.3 Experimental Methodology ..................... 49 3.3.1 Target Testing ........................ 49 3.3.2 Automatic query generation ................. 49 3.3.3 User/Machine models .................... 50 3.4 Experiments .............................. 51 3.4.1 Experimental results ..................... 51 3.5 Discussion ............................... 60 4 Query Classi¯cation 62 4.1 Introduction .............................. 62 4.2 Related Work ............................. 64 4.3 Query Enrichment .......................... 66 4.3.1 Pseudo-Relevance Feedback ................. 66 4.3.2 Term Co-occurrence from Secondary Sources ....... 68 4.4 The SVM Query Classi¯er ...................... 68 4.4.1 Data representation ..................... 68 vii 4.4.2 Multi-label, Multi-class Classi¯cation ............ 70 4.4.3 Evaluation Criteria ...................... 70 4.5 Experiments .............................. 71 4.5.1 Experimental Data Set .................... 71 4.5.2 Experimental Setting ..................... 72 4.5.3 Experimental Results .................... 73 4.6 Topic Analysis of Search Queries .................. 78 4.6.1 Results ............................ 79 4.7 Discussion ............................... 94 5 Ranking Classes of Search Engine Results 96 5.1 Introduction .............................. 96 5.1.1 Related Work ......................... 97 5.2 Class-Based Ranking Method .................... 98 5.2.1 Query-Dependent Rank ................... 99 5.2.2 Query-Independent Rank .................. 100 5.2.3 Additional Class Ranking Models .............. 102 5.3 Experiments .............................. 104 5.3.1 Experimental Setting ..................... 104 5.3.2 Experimental Methodology ................. 106 5.3.3 Experimental Results .................... 106 5.4 Learning to Rank ........................... 111 5.5 A Proof of Concept for a Classi¯cation-Based Search Engine .. 113 5.5.1 Classi¯cation-Based Search Engines - an Architecture .. 113 5.5.2 Classi¯cation-Based Search Engines - an Implementation 116 5.6 Discussion ............................... 118 viii 6 Concluding Remarks and Future Directions 121 ix List of Figures 2.1 Real information need, perceived information need, request, and query [90] ............................... 12 2.2 Text analysis process ......................... 21 2.3 Optimal separating hyperplane in a two-dimensional space .... 28 3.1 Conceptual illustration of a classi¯cation-based IR system .... 42 3.2 The Illustration of Ranks ...................... 44 3.3 Results using the Open Directory oracle classi¯er ......... 55 3.4 Results for the KNN classi¯er with non-disjoint classes ...... 58 3.5 Results for the KNN classi¯er in real case ............. 59 4.1 Distribution of number of clicks per query for the di®erent cate- gories ................................. 85 4.2 Average clickthrough rate of clicked queries ............ 86 4.3 Clickthrough position - all clickthroughs .............. 89 4.4 Clickthrough position - ¯rst clickthrough for each clicked query . 90 4.5 Daily volume of the queries in speci¯c categories ......... 93 5.1 The cumulative distribution of the rank of click through based on ranking position (multiple-click). ................ 109 x 5.2 The Cumulative Distribution of Click Through Based on Ranking Position (one-click). ......................... 111 5.3 An architecture of a classi¯cation-based metasearch engine. ... 114 5.4 Google Search User Interface .................... 115 5.5 Search Engine Results Pages (SERP) ...............
Recommended publications
  • DEWS: a Decentralized Engine for Web Search

    DEWS: a Decentralized Engine for Web Search

    DEWS: A Decentralized Engine for Web Search Reaz Ahmed, Rakibul Haque, Md. Faizul Bari, Raouf Boutaba David R. Cheriton School of Computer Science, University of Waterloo [r5ahmed|m9haque|mfbari|rboutaba]@uwaterloo.ca Bertrand Mathieu Orange Labs, Lannion, France [email protected] (Technical Report: CS-2012-17) Abstract The way we explore the Web is largely governed by centrally controlled, clustered search engines, which is not healthy for our freedom in the Internet. A better solution is to enable the Web to index itself in a decentralized manner. In this work we propose a decentralized Web search mechanism, named DEWS, which will enable the existing webservers to collaborate with each other to form a distributed index of the Web. DEWS can rank the search results based on query keyword relevance and relative importance of websites. DEWS also supports approximate matching of query keywords and incremental retrieval of search results in a decentralized manner. We use the standard LETOR 3.0 dataset to validate the DEWS protocol. Simulation results show that the ranking accuracy of DEWS is very close to the centralized case, while network overhead for collaborative search and indexing is logarithmic on network size. Simulation results also show that DEWS is resilient to changes in the available pool of indexing webservers and works efficiently even in presence of heavy query load. 1 Introduction Internet is the largest repository of documents that man kind has ever created. Voluntary contributions from millions of Internet users around the globe, and decentralized, autonomous hosting infrastructure are the sole factors propelling the continuous growth of the Internet.
  • Towards the Ontology Web Search Engine

    Towards the Ontology Web Search Engine

    TOWARDS THE ONTOLOGY WEB SEARCH ENGINE Olegs Verhodubs [email protected] Abstract. The project of the Ontology Web Search Engine is presented in this paper. The main purpose of this paper is to develop such a project that can be easily implemented. Ontology Web Search Engine is software to look for and index ontologies in the Web. OWL (Web Ontology Languages) ontologies are meant, and they are necessary for the functioning of the SWES (Semantic Web Expert System). SWES is an expert system that will use found ontologies from the Web, generating rules from them, and will supplement its knowledge base with these generated rules. It is expected that the SWES will serve as a universal expert system for the average user. Keywords: Ontology Web Search Engine, Search Engine, Crawler, Indexer, Semantic Web I. INTRODUCTION The technological development of the Web during the last few decades has provided us with more information than we can comprehend or manage effectively [1]. Typical uses of the Web involve seeking and making use of information, searching for and getting in touch with other people, reviewing catalogs of online stores and ordering products by filling out forms, and viewing adult material. Keyword-based search engines such as YAHOO, GOOGLE and others are the main tools for using the Web, and they provide with links to relevant pages in the Web. Despite improvements in search engine technology, the difficulties remain essentially the same [2]. Firstly, relevant pages, retrieved by search engines, are useless, if they are distributed among a large number of mildly relevant or irrelevant pages.
  • Merging Multiple Search Results Approach for Meta-Search Engines

    Merging Multiple Search Results Approach for Meta-Search Engines

    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by D-Scholarship@Pitt MERGING MULTIPLE SEARCH RESULTS APPROACH FOR META-SEARCH ENGINES By Khaled Abd-El-Fatah Mohamed B.S-------- Cairo University, Egypt, 1995 M.A-------Cairo University, Egypt, 1999 M.A------- University of Pittsburgh 2001 Submitted to the Graduate Faculty of School of Information Sciences in Partial Fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2004 UNIVERSITY OF PITTSBURGH INFORMATION SCIENCES This dissertation was presented by Khaled Abd-El-Fatah Mohamed It was defended on Janauary 29, 2004 and approved by Chris Tomer, PhD, Associate Professor, DLIS Jose-Marie Griffiths, PhD, Professor, DLIS Don King, Research Professor, DLIS Amy Knapp, PhD, ULS Dissertation Director: Chris Tomer, PhD, Associate Professor MERGING MULTIPLE SEARCH RESULTS APPROACH FOR META-SEARCH ENGINES Khaled A. Mohamed, PhD University of Pittsburgh, 2004 Meta Search Engines are finding tools developed for enhancing the search performance by submitting user queries to multiple search engines and combining the search results in a unified ranked list. They utilized data fusion technique, which requires three major steps: databases selection, the results combination, and the results merging. This study tries to build a framework that can be used for merging the search results retrieved from any set of search engines. This framework based on answering three major questions: 1. How meta-search developers could define the optimal rank order for the selected engines. 2. How meta-search developers could choose the best search engines combination. 3. What is the optimal heuristic merging function that could be used for aggregating the rank order of the retrieved documents form incomparable search engines.
  • A Meta Search Engine Based on a New Result Merging Strategy

    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.
  • Organizing User Search Histories

    Organizing User Search Histories

    Global Journal of Computer Science and Technology Network, Web & Security Volume 13 Issue 13 Version 1.0 Year 2013 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 0975-4172 & Print ISSN: 0975-4350 Organizing user Search Histories By Ravi Kumar Yandluri Gokaraju Rangaraju Institute of Engineering & Technology, India Abstract - Internet userscontinuously make queries over web to obtain required information. They need information about various tasks and sub tasks for which they use search engines. Over a period of time they make plenty of related queries. Search engines save these queries and maintain user’s search histories. Users can view their search histories in chronological order. However, the search histories are not organized into related groups. In fact there is no organization made except the chronological order. Recently Hwang et al. studied the problem of organizing historical search information of users into groups dynamically. This automatic grouping of user search histories can help search engines also in various applications such as collaborative search, sessionization, query alterations, result ranking and query suggestions. They proposed various techniques to achieve this. In this paper we implemented those techniques practically using a prototype web application built in Java technologies. The experimental results revealed that the proposed application is useful to organize search histories. Indexterms : search engine, search history, click graph, query grouping. GJCST-E Classification : H.3.5 Organizing user Search Histories Strictly as per the compliance and regulations of: © 2013. Ravi Kumar Yandluri. This is a research/review paper, distributed under the terms of the Creative Commons Attribution- Noncommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction inany medium, provided the original work is properly cited.
  • A Community-Based Approach to Personalizing Web Search

    A Community-Based Approach to Personalizing Web Search

    COVER FEATURE A Community-Based Approach to Personalizing Web Search Barry Smyth University College Dublin Researchers can leverage the latent knowledge created within search communities by recording users’search activities—the queries they submit and results they select—at the community level.They can use this data to build a relevance model that guides the promotion of community-relevant results during regular Web search. ver the past few years, current Web search through rates should be preferred during ranking. engines have become the dominant tool for Unfortunately, Direct Hit’s so-called popularity engine accessing information online. However, even did not play a central role on the modern search stage today’s most successful search engines struggle (although the technology does live on as part of the O to provide high-quality search results: Approx- Teoma search engine) largely because the technology imately 50 percent of Web search sessions fail to find proved inept at identifying new sites or less well- any relevant results for the searcher. traveled ones, even though they may have had more The earliest Web search engines adopted an informa- contextual relevance to a given search. tion-retrieval view of search, using sophisticated term- Despite Direct Hit’s fate, the notion that searchers based matching techniques to identify relevant docu- themselves could influence the ranking of results by virtue ments from repeated occurrences of salient query terms. of their search activities remained a powerful one. It res- Although such techniques proved useful for identifying onates well with the ideas that underpin the social web, a set of potentially relevant results, they offered little a phrase often used to highlight the importance of a suite insight into how such results could be usefully ranked.
  • Web Query Classification and URL Ranking Based on Click Through Approach

    Web Query Classification and URL Ranking Based on Click Through Approach

    S.PREETHA et al, International Journal of Computer Science and Mobile Computing Vol.2 Issue. 10, October- 2013, pg. 138-144 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IJCSMC, Vol. 2, Issue. 10, October 2013, pg.138 – 144 RESEARCH ARTICLE Web Query Classification and URL Ranking Based On Click through Approach S.PREETHA1, K.N Vimal Shankar2 PG Scholar1, Asst. Professor2 Department of Computer Science & Engineering1, 2 V.S.B. Engineering College, Karur, India1, 2 [email protected], [email protected] Abstract― In a web based application; different users may have different search goals when they submit it to a search engine. For a broad-topic and ambiguous query it is difficult. Here we propose a novel approach to infer user search goals by analyzing search engine query logs. This is typically shown in cases such as these: Different users have different backgrounds and interests. However, effective personalization cannot be achieved without accurate user profiles. We propose a framework that enables large-scale evaluation of personalized search. The goal of personalized IR (information retrieval) is to return search results that better match the user intent. First, we propose a framework to discover different user search goals for a query by clustering the proposed feedback sessions. Feedback sessions are getting constructed from user click-through logs and can efficiently reflect the information needs of users. Second, we propose an approach to generate pseudo-documents to better represent the feedback sessions for clustering.
  • Classifying User Search Intents for Query Auto-Completion

    Classifying User Search Intents for Query Auto-Completion

    Classifying User Search Intents for Query Auto-Completion Jyun-Yu Jiang Pu-Jen Cheng Department of Computer Science Department of Computer Science University of California, Los Angeles National Taiwan University California 90095, USA Taipei 10617, Taiwan [email protected] [email protected] ABSTRACT completion is to accurately predict the user's intended query The function of query auto-completion1 in modern search with only few keystrokes (or even without any keystroke), engines is to help users formulate queries fast and precisely. thereby helping the user formulate a satisfactory query with Conventional context-aware methods primarily rank candi- less effort and less time. For instance, users can avoid mis- date queries2 according to term- and query- relationships to spelling and ambiguous queries with query completion ser- the context. However, most sessions are extremely short. vices. Hence, how to capture user's search intents for pre- How to capture search intents with such relationships be- cisely predicting the query one is typing is very important. The context information, such as previous queries and comes difficult when the context generally contains only few 3 queries. In this paper, we investigate the feasibility of dis- click-through data in the same session , is usually utilized covering search intents within short context for query auto- to capture user's search intents. It also has been exten- completion. The class distribution of the search session (i.e., sively studied in both query completion [1, 21, 35] and sug- gestion [18, 25, 37]. Previous work primarily focused on cal- issued queries and click behavior) is derived as search in- 2 tents.
  • Do Metadata Models Meet IQ Requirements?

    Do Metadata Models Meet IQ Requirements?

    Do Metadata Mo dels meet IQ Requirements Claudia Rolker Felix Naumann Humb oldtUniversitat zu Berlin Forschungszentrum Informatik FZI Unter den Linden HaidundNeuStr D Berlin D Karlsruhe Germany Germany naumanndbisinformatikhub erli nde rolkerfzide Abstract Research has recognized the imp ortance of analyzing information quality IQ for many dierent applications The success of data integration greatly dep ends on the quality of the individual data In statistical applications p o or data quality often leads to wrong conclusions High information quality is literally a vital prop erty of hospital information systems Po or data quality of sto ck price information services can lead to economically wrong decisions Several pro jects have analyzed this need for IQ metadata and have prop osed a set of IQ criteria or attributes which can b e used to prop erly assess information quality In this pap er we survey and compare these approaches In a second step we take a lo ok at existing prominent prop osals of metadata mo dels esp ecially those on the Internet Then we match these mo dels to the requirements of information quality mo deling Finally we prop ose a quality assurance pro cedure for the assurance of metadata mo dels Intro duction The quality of information is b ecoming increasingly imp ortant not only b ecause of the rapid growth of the Internet and its implication for the information industry Also the anarchic nature of the Internet has made industry and researchers aware of this issue As awareness of quality issues amongst information
  • Meta Search Engine Examples

    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

    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.
  • List of Search Engines

    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 .