Learning to Search and Searching to Learn: Income, Education, and Experience Online

Using data from the Pew Internet and American Life Project surveys, this article explores changing trends in reported sophistication and satisfaction with search skills and with search engines. We find that the proportion of Internet users searching online for answers to specific questions—as opposed to casual browsing—has grown significantly. Moreover, as users get more experience online, they increasingly become dependent on search engines, confident in their findings, and savvy about how search engines structure information, privilege paid results, and track users. When other factors are controlled, years of online experience is a strong predictor of the likelihood of a person doing specific searches on a daily basis, and experience can have an even stronger positive effect than education and income. We also find that years of online experience, frequency of use, and sophistication with multiple search engines can overcome socio-economic status in predicting how active a person is in searching across different topics.     

The theory of planned behaviour applied to search engines as a learning tool

Abstract Search engines have been developed for helping learners to seek online information. Based on theory of planned behaviour approach, this research intends to investigate the behaviour of using search engines as a learning tool. After factor analysis, the results suggest that perceived satisfaction of search engine, search engines as an information retrieval tool, and sharing search experience and information are three significant factors to affect individual behavioural intention to use search engines as a learning tool. In addition, the research elaborates that individual Internet and search engine experiences will affect these three factors. Moreover, the result also offers evidence that today's computers are popular for the application of communication and information retrieval.     

Information retrieval from the World Wide Web: a user-focused approach based on individual experience with search engines

Although search engines are essential tools for finding information on the World Wide Web, the effective use of search engines for information retrieval (IR) is a crucial challenge for any Internet user. Based on the user-focused approach, this study investigates individual information retrieval behaviors using information processing theory. The results show that experience with search engines significantly affects users’ attitudes toward search engines for information retrieval, the query-based service is more popular than the directory-based service, users are not completely satisfied with the precision of retrieved information and the response time of search engines, and users’ motivation is a key factor that predicts their intention to use search engines for information retrieval. Furthermore, this study proposes a conceptual model for investigating individual attitudes toward search engines for information retrieval.     

基于搜索引擎的信息查询技术研究

随着网络的快速发展,搜索引擎日益成为处理信息的主流工具。Internet是世界上资料最多、规模最大的信息资料库。在WWW上进行信息查找有三种方法,即基于超文本的信息查询、基于目录的信息查询、基于搜索引擎的信息查询,网络信息检索核心工具是搜索引擎。本文从搜索引擎概述、查询技术方法及展望三方面进行阐述。     

An investigation of user attitudes toward search engines as an information retrieval tool

After the Internet has gained great popularity at homes and schools, there is much information on the Web. Today, one of the primary uses of the Internet is information retrieval from search engines. The main purpose of the current study is to develop and examine an individual attitude model towards search engines as a tool for retrieving information. This model integrates individual computer experience with perceptions. In addition, it also combines perception theories, such as technology acceptance model (TAM) and motivation, in order to understand individual attitudes toward search engines. The results show that individual computer experience, quality of search systems, motivation, and perceptions of technology acceptance are all key factors that affect individual feelings to use search engines as an information retrieval tool.     

The “Panopticon” of search engines: the response of the European data protection framework

Nowadays, Internet users are depending on various search engines in order to be able to find requested information on the Web. Although most users feel that they are and remain anonymous when they place their search queries, reality proves otherwise. The increasing importance of search engines for the location of the desired information on the Internet usually leads to considerable inroads into the privacy of users. A heated debate is currently ongoing at European level regarding the question if search engine providers that are established outside the European Union are covered by the European data protection framework and the obligations it imposes on entities that process personal data. The scope of this paper is to examine the applicability of the European data protection legislation to non-EU-based search engine providers and to study the main privacy issues with regard to search engines, such as the character of search logs, their anonymisation and their retention period. Ixquick, a privacy-friendly meta-search engine, will be presented as an alternative to privacy intrusive existing practices of search engines.     

A statistical method for estimating the usefulness of text databases

Searching desired data on the Internet is one of the most common ways the Internet is used. No single search engine is capable of searching all data on the Internet. The approach that provides an interface for invoking multiple search engines for each user query has the potential to satisfy more users. When the number of search engines under the interface is large, invoking all search engines for each query is often not cost effective because it creates unnecessary network traffic by sending the query to a large number of useless search engines and searching these useless search engines wastes local resources. The problem can be overcome if the usefulness of every search engine with respect to each query can be predicted. We present a statistical method to estimate the usefulness of a search engine for any given query. For a given query, the usefulness of a search engine in this paper is defined to be a combination of the number of documents in the search engine that are sufficiently similar to the query and the average similarity of these documents. Experimental results indicate that our estimation method is much more accurate than existing methods.     

基于知识的网页检索工具

随着因特网在全球范围的广泛使用,越来越多的人们借助于因特网从事科研和商务活动,而网页检索工具成了人们必不可少的软件工具．然而,目前流行的检索工具大多基于关键字查询,常常出现信息过载或有用信息丢失等现象．造成这一原因主要有两方面：用户提交的查询不能很好地表达他的目的;查询的结果没有建立有效的索引机制,引导人们快速找到有用信息。为此我们提出一种基于知识的网页检索工具（ＫＷＳＥ）,它是在已有的检索工具的     

A Relation-Based Search Engine in Semantic Web

With the development of the Web, an information "Big Bang" has taken place on the Internet. Search engines have become one of the most helpful tools for obtaining useful information from the Internet. However, instead of caring about the semantics of information, the machine on the current Web cares about the location and display of information only. Because of this shortcoming of the current Web, the search results by even the most popular search engines cannot produce satisfactory results. The development of the next generation Web, semantic Web, will turn the situation around completely. This paper proposes a prototype relation-based search engine, "OntoLook," which has been implemented in a virtual semantic Web environment in our lab. We also present its system architecture and analyze the key algorithm     

Internet use, videogame playing and cell phone use as predictors of children’s body mass index (BMI), body weight, academic performance, and social and overall self-esteem

In this research we examined the prediction of children’s body mass index (BMI), body weight, academic performance, social self-esteem and overall self-esteem from their IT (information technology) use, specifically, their Internet use, cell phone use and videogame playing, after controlling for the effects of gender, race, age and household income on these measures. Participants were 482 children whose average age was 12 years old. One-third was African American and two-thirds were Caucasian American. Results indicated that IT use did not predict BMI or body weight, contrary to one previous survey and the widespread belief that screen time is responsible for the obesity epidemic among our nation’s children. Instead, BMI and body weight were higher for African Americans, older children and children from lower income households. The sole and strong positive predictor of visual-spatial skills was videogame playing, which also predicted lower grade point averages (GPAs). Gender and Internet use predicted standardized test scores in reading skills. Females and children who used the Internet more had better reading skills than did males and children who used the Internet less, respectively. Implications of these findings for future research on the benefits and liabilities of IT use are discussed.     

基于Agent的Internet信息检索

Intemet上有很多搜索引擎用于帮助人们发现和收集Intemet上的各种信息，但它们还存在着一些缺陷，如信息导引能力差；信息的精确度不高；一般不具备学习功能。该文提出一种用于信息检索的Multi-Agent系统，从Intemet上发现和收集信息。采用Multi-Agent体系结构，该系统能够根据用户的需要和任务模型主动、智能和协作地从Intemet上各种信息资源(如数据库，WWW服务器等)发现和收集信息，并对搜集到的信息进行管理和维护。该文主要针对多主体的协作性、互操作性和运行环境，讨论Intemet信息收集的解决方案。     

Patterns of Information Search and Access on the World Wide Web: Democratizing Expertise or Creating New Hierarchies?*1

Will the World Wide Web and search engines foster access to more diverse sources of information, or have a centralizing influence through a ‘winner‐take‐all’ process? To address this question, we examined how search engines are used to access information about six global issues (climate change, poverty, HIV/AIDS, terrorism, trade reform, and Internet and society). The study used a combination of webmetric analyses and interviews with experts. From interviews we were able to explore how experts on these topics use search engines within their specialist fields. Using webmetric analysis, we were able to compare the results from a number of search engines and show how the top ranked sites are clustered as well as the distribution of their connectivity. Results suggest that the Web tends to reduce the significance of offline hierarchies in accessing information – thereby “democratizing” access to worldwide resources. It also seems, however, that centers of expertise progressively refine their specializations, gaining a ‘winner‐take‐all’ status within a narrower area. Some limitations of the winner‐take‐all thesis for access to research are discussed.     

Search Engine or Content Website? A Local Information Seeking Classification Model Based on Consumer Characteristics and Website Perceptions

Decisions concerning everyday life activities such as patronizing restaurants require obtaining information about them. Some consumers go directly to content websites when they need such information; others go directly to search engines. How do search engine users differ from content website users for a given type of local information? This local information-seeking classification model posits that they differ in their prior experiences with their “go-to” websites, their perceived search skills, their habit of using search engines, their involvement with the activity for which information is sought, their tendency to conduct extensive information search, and their beliefs about their “go-to” website types. Empirical results support the model. By integrating everyday life information seeking (ELIS), technology acceptance model (TAM), and consumer behavior literatures, the model in this study fills a theoretical gap in the literature and opens new lines of inquiries for both ELIS and TAM research.     

基于多Agent的搜索引擎设计与实现

搜索引擎的出现改变了人们获取信息的方式,利用搜索引擎可以快速地找到需要的信息,为我们在Intemet上获取信息提供了一种有效的手段。但随着Intemet的发展和网上信息量的激增,人们在使用中却发现要准确、快速地查找自己所需的信息是越来越困难。文章依据搜索引擎、Agent技术,提出了基于多Agent技术的智能搜索引擎概念,能够有效地提高搜索引擎的搜索质量和用户服务,为解决当前搜索引擎存在的一些问题提供了一种新的有效的方法     

Finding story chains in newswire articles using random walks

Massive amounts of information about news events are published on the Internet every day in online newspapers, blogs, and social network messages. While search engines like Google help retrieve information using keywords, the large volumes of unstructured search results returned by search engines make it hard to track the evolution of an event. A story chain is composed of a set of news articles that reveal hidden relationships among different events. Traditional keyword-based search engines provide limited support for finding story chains. In this paper, we propose a random walk based algorithm to find story chains. When breaking news happens, many media outlets report the same event. We have two pruning mechanisms in the algorithm to automatically exclude redundant articles from the story chain and to ensure efficiency of the algorithm. We further explore how named entities and word relevance can help find relevant news articles and improve algorithm efficiency by creating a co-clustering based correlation graph. Experimental results show that our proposed algorithm can generate coherent story chains without redundancy. The efficiency of the algorithm is significantly improved on the correlation graph.     

Retracted: An Empirical Study of an Internet Marketing Strategy for Search Engine Optimization

Over the past few years, more and more Internet visitors are reaching websites through search engines rather than through direct links from another web page. Search engines have come to occupy a prominent position in the online world and are being used to find all kinds of information including things, events, people, and places. The search engine is also coming to play a greater role as a critical link between firms that use the Internet to build their image and find their target customers. How to achieve a high ranking in such search results given certain search words or phrases has become an issue of much interest in Internet marketing. The purpose of the current study is to develop a search engine optimization (SEO) mechanism that can be used by an enterprise to improve the ranking of its website in the search engine results. Social networking sites are included in our exploration of Internet marketing strategy. The proposed mechanism is then applied in the operations of an online ebook store. The website rankings obtained from two well‐known online search engines (Google and Yahoo) are evaluated in efforts to explore a better strategy to ensure higher rankings. The results reveal that a well‐designed SEO strategy, with the incorporation of social networking, can effectively enhance the website's visibility and exposure. Such a strategy will eventually contribute to overall site traffic and improve interaction with customers. © 2012 Wiley Periodicals, Inc.     

知识图谱研究综述

随着信息化社会的来临,各种互联网技术应运而生,数字信息已然成为当今社会中商家必争的宝贵财富资源。众多数字信息中,怎样帮助用户精准筛选出有效信息是当前搜索引擎所面临的巨大挑战。传统的互联网搜索仅仅是基于本文的链接,搜索时仅单纯的给出包含搜索词的网页,让用户去网页中寻找答案,这种检索方法耗时耗力,还不能准确给出用户想要的答案。由此谷歌率先提出以知识图谱(Knowledge Graph)为技术基础的的搜索引擎,这是搜索引擎界的一次重大变革。它以图的形式表现客观世界中的概念和实体及其之间关系,现如今广泛应用于语义搜索、智能问答、决策支持等智能服务领域。本文针对什么是知识图谱、如何表示构建知识图谱及知识图谱的主要应用作了详细阐述,希望更多的读者可以了解知识图谱及其对人工智能发展的巨大贡献。     

Design and Development of a Cross Search Engine for Multiple Heterogeneous Databases Using UML and Design Patterns

As the popularity and complexity of Internet search engines increase, the design, development and maintenance of large, complex web-based Information Retrieval (WIR) systems become a challenge. The difficulty of designing a WIR system is compounded by information overload triggered from various different information sources. From a standpoint of the search engine users, it is more usable for the WIR to provide a single search point to multiple databases. To tackle this issue, we present the design and implementation of a cross-search component for the CS-Engine (Cross-Search Engine). The CS-Engine allows the user to search heterogeneous, multiple databases with one command. The CS-Engine is also distinguished from meta-search engines in that the CS-Engine does not need to trigger other search engines and translate a query for other search engines. Our performance benchmark tests show that the CS-Engine is scalable and usable. We also compare CS-engine with other search engines such as Google and AltaVista. The CS-Engine was developed with UML and design patterns including: (1) use case diagram, (2) class diagram, (3) package diagram, (4) interaction diagram, (5) Factory pattern, and (6) Strategy patterns. We conclude our paper with technical lessons learned as well as organizational issues encountered during the development phase.     

基于Lucene的搜索引擎设计与实现

当今搜索引擎已经成为人们在网上搜索信息的重要工具。通用的搜索引擎虽然功能强大,但对具有很多子网站的企业门户网站进行搜索时响应速度慢,索引范围不全。Lucene是一个强大的全文索引引擎工具包,应用它可以快速地开发一个搜索引擎。文中描述了利用基于Java的全文检索工具包Lucene开发定制的中文搜索引擎方法,并且将该定制的搜索引擎与Google的站内搜索进行试验比较,发现在对具有很多子网站的企业门户网站进行搜索时有优于Google的性能。     

一种基于常用搜索引擎的智能信息检索系统

针对用户利用常用搜索引擎查询信息时，搜索引擎返回海量杂乱、无序的网页，用户难以从中快速、准确地获得真正关心的信息的现状，从Internet用户的兴趣度出发，设计了一种基于近似网页聚类算法的智能搜索系统。该系统在用户利用常用搜索引擎系统进行信息检索时，消除搜索引擎返回的重复页，对剩余页面进行聚类，返回给用户聚类后的网页簇，这样用户就可以选择浏览自己感兴趣的页面，从而大大提高了信息检索的查准率；实验证明该系统在保证查全率和查准率的基础上大大提高了搜索效率。