Tuning up the search engine

Advances in Internet search engine technology may not help you blast Klingons into outer space, but they should help you find them more quickly on the Web. The whole arena for Internet searching has become rather interesting. Search engines appear poised to make some serious breakthroughs in relevancy ranging and personalization that promise to increase the accuracy and reliability of search. On the ether hand, data suggests that users are becoming increasingly disenchanted with search engines that don't actually search the Web, but rather search records of the Web sites their robots have visited. Some online merchants (Victoria's Secret, for example) don't even enable keyword searches on their sites. The Web's increasingly dynamic nature complicates searching. New pages created on the fly using personalization information, and even static content, with dynamically inserted sidebars, navigation bars, advertising and commentary, can present a rapidly changing picture for any robot to discover. And as indexes grow larger, search system performance becomes a significant problem     

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.     

A Framework for Cross-Language Information Access: Application to English and Japanese

Internet search engines allow access to online information from all over the world. However, there is currently a general assumption that users are fluent in the languages of all documentsthat they might search for. This has for historical reasons usually been a choice between English and the locally supported language. Given the rapidly growing size of the Internet, it is likely that future users will need to access information in languages in which they are not fluent or have no knowledge of at all. This papershows how information retrieval and machine translation can becombined in a cross-language information access frameworkto help overcome the language barrier. We presentencouraging preliminary experimental results using English queries toretrieve documents from the standard Japanese language BMIR-J2retrieval test collection. We outline the scope and purpose ofcross-language information access and provide an example applicationto suggest that technology already exists to provide effective andpotentially useful applications.     

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。     

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.     

基于聚类的个性化元搜索引擎设计

Internet上信息资源的飞速膨胀造成用户在进行信息检索时的不便,传统的搜索引擎不能很好地解决这个问题。因此提出了一种基于聚类的个性化元搜索引擎模型,系统通过对用户建立个人模型,对此模型进行聚类形成不同用户群,并对检索到的结果进行聚类处理,同用户模型聚类相结合返回给用户个性化的搜索结果。分析了个性化元搜索引擎的系统构成,详细介绍了每个模块的功能,最后展望了它的发展前景。     

Who and what links to the Internet Archive

The Internet Archive’s (IA) Wayback Machine is the largest and oldest public Web archive and has become a significant repository of our recent history and cultural heritage. Despite its importance, there has been little research about how it is discovered and used. Based on Web access logs, we analyze what users are looking for, why they come to IA, where they come from, and how pages link to IA. We find that users request English pages the most, followed by the European languages. Most human users come to Web archives because they do not find the requested pages on the live Web. About 65 % of the requested archived pages no longer exist on the live Web. We find that more than 82 % of human sessions connect to the Wayback Machine via referrals from other Web sites, while only 15 % of robots have referrers. Most of the links (86 %) from Websites are to individual archived pages at specific points in time, and of those 83 % no longer exist on the live Web. Finally, we find that users who come from search engines browse more pages than users who come from external Web sites.     

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。     

Web服务搜索技术综述

随着Web服务应用的迅速发展与日益普及,如何快速、准确地搜索到用户所需的Web服务成为了制约Web服务发展的关键问题之一。目前的Web服务搜索技术包括：基于UDDI注册中心、通过Web服务网站、使用专用搜索引擎与使用通用搜索引擎四种方式。对现有主要Web服务搜索技术进行了详细评述。在对典型Web服务搜索技术分析比较的基础上,指出了建立专用的Web服务搜索引擎的必要性以及所面临的问题与挑战。     

Web服务搜索技术综述*

随着Web服务应用的迅速发展与日益普及, 如何快速、准确地搜索到用户所需的Web服务成为了制约Web服务发展的关键问题之一。目前的Web服务搜索技术包括：基于UDDI注册中心、通过Web服务网站、使用专用搜索引擎与使用通用搜索引擎四种方式。对现有主要Web服务搜索技术进行了详细评述。在对典型Web服务搜索技术分析比较的基础上, 指出了建立专用的Web服务搜索引擎的必要性以及所面临的问题与挑战。     

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     

Personalized mining of web documents using link structures and fuzzy concept networks

Personalized search engines are important tools for finding web documents for specific users, because they are able to provide the location of information on the WWW as accurately as possible, using efficient methods of data mining and knowledge discovery. The types and features of traditional search engines are various, including support for different functionality and ranking methods. New search engines that use link structures have produced improved search results which can overcome the limitations of conventional text-based search engines. Going a step further, this paper presents a system that provides users with personalized results derived from a search engine that uses link structures. The fuzzy document retrieval system (constructed from a fuzzy concept network based on the user's profile) personalizes the results yielded from link-based search engines with the preferences of the specific user. A preliminary experiment with six subjects indicates that the developed system is capable of searching not only relevant but also personalized web pages, depending on the preferences of the user.     

元搜索中成员搜索引擎的选择问题研究

随着网络的普及,网上检索成为了人们获取信息的主要方式。目前的搜索引擎相对独立,覆盖范围比较有限。相比之下,元搜索能够更好地满足用户的检索需求。当用户在元搜索提供的统一界面中输入一个查询时,元搜索会将处理后的用户请求发送给相关的成员搜索引擎。但是一个重要的问题是如何识别出潜在的搜索引擎以便更好地处理用户的请求。鉴于此提出了一种基于遗传算法的选择机制,该方法将各个成员搜索引擎的权重考虑在内。实验结果表明,该方法确实能够提高引擎选择中的效率和精度。     

Implicit: a multi-agent recommendation system for web search

For people with non-ordinary interests, it is hard to search for information on the Internet because search engines are impersonalized and are more focused on “average” individuals with “standard” preferences. In order to improve web search for a community of people with similar but specific interests, we propose to use the implicit knowledge contained in the search behavior of groups of users. We developed a multi-agent recommendation system called Implicit, which supports web search for groups or communities of people. In Implicit, agents observe behavior of their users to learn about the “culture” of the community with specific interests. They facilitate sharing of knowledge about relevant links within the community by means of recommendations. The agents also recommend contacts, i.e., who in the community is the right person to ask for a specific topic. Experimental evaluation shows that Implicit improves the quality of the web search in terms of precision and recall.     

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.     

The Social, Political, Economic, and Cultural Dimensions of Search Engines: An Introduction

Search engines are some of the most popular destinations on the Web—understandably so, given the vast amounts of information available to users and the need for help in sifting through online content. While the results of significant technical achievements, search engines are also embedded in social processes and institutions that influence how they function and how they are used. This special theme section of the Journal of Computer-Mediated Communication explores these non-technical aspects of search engines and their uses.     

用信息-摘要算法提高Web信息检索效率的研究

针对常用搜索引擎返回给用户的信息中包含大量重复网页的缺陷,提出了一种基于信息-摘要算法的去除重复网页算法。由于算法的成熟,该算法易实现,可移植性强。实验证明该算法能有效地去除常用搜索引擎返回的重复网页,从而为Internet用户提高信息检索效率,具有较强的实用价值。     

互联网应用发展和用户行为解析

互联网在我国迅猛发展,其应用不断创新发展。本文从搜索引擎、社交网站、电子商务、网络视频、网络游戏、移动互联网6个方面对互联网应用发展现状以及各种用户的行为习惯进行研究．最后分析了各种互联网应用的发展趋势。     

Web accessibility awareness in search engine results

The enormous amount of information available on the Internet requires the use of search engines in order to find specific information. As far as web accessibility is concerned, search engines contain two kinds of barriers: on the one hand, the interfaces for making queries and accessing results are not always accessible; on the other hand, web accessibility is not taken into account in information retrieval (IR) processes. Consequently, in addition to interface problems, accessing the items in the list of results tends to be an unsatisfactory experience for people with disabilities. Some groups of users cannot take advantage of the services provided by search engines, as the results are not useful due to their accessibility restrictions. The goal of this paper is to propose the integration of web accessibility measurement into information retrieval processes. Firstly, quantitative accessibility metrics are defined in order to accurately measure the accessibility level of web pages. Secondly, a model to integrate these metrics within IR processes is proposed. Finally, a prototype search engine which re-ranks results according to their accessibility level based on the proposed model is described.     

LESSON: A system for lecture notes searching and sharing over Internet

In this paper, we present a system LESSON for lecture notes searching and sharing, which is dedicated to both instructors and students for effectively supporting their Web-based teaching and learning activities. The LESSON system employs a metasearch engine for lecture notes searching from Web and a peer-to-peer (P2P) overlay network for lecture notes sharing among the users. A metasearch engine provides an unified access to multiple existing component search engines and has better performance than general-purpose search engines. With the help of a P2P overlay network, all computers used by instructors and students can be connected into a virtual society over the Internet and communicate directly with each other for lecture notes sharing, without any centralized server and manipulation. In order to merge results from multiple component search engines into a single ranked list, we design the RSF strategy that takes rank, similarity and features of lecture notes into account. To implement query routing decision for effectively supporting lecture notes sharing, we propose a novel query routing mechanism. Experimental results indicate that the LESSON system has better performance in lecture notes searching from Web than some popular general-purpose search engines and some existing metasearch schemes; while processing queries within the system, it outperforms some typical routing methods. Concretely, it can achieve relatively high query hit rate with low bandwidth consumption in different types of network topologies.