关于搜索引擎的研究综述

由于目前搜索服务被越来越多的用户所认识和青睐，各样的搜索引擎也应运而生。文中阐述了搜索引擎的工作原理，并对搜索引擎按照不同的依据对其进行分类。介绍并比较目前较为有名气同时其发展历史也推进了搜索引擎的发展的几个搜索引擎，最后提出目前搜索引擎所存在的问题。     

一种基于Agent的分布式搜索引擎

分析了传统搜索引擎的不足，提出了一种基于Agent的分布式搜索引擎以Agent的方法实现了搜索引擎的各项功能，描述了多个搜索引擎利用Agent通信机制组成分布式网络的过程。     

多元搜索引擎的现状及其典型系统的介绍

本文介绍了搜索引擎发展过程中出现的一种新技术——多元搜索引擎。分析了多元搜索引擎的现状，并对典型的多元搜索引擎系统进行了介绍。同时提出了多元搜索引擎发展过程中有待改进的一面。     

基于Intranet的搜索引擎

分析了Intranet的特点，指出了Intranet中搜索引擎设计的特殊性。目前Intranet上的搜索引擎存在着许多问题，提出了如何在Intranet内部搜索引擎的设计上解决这些问题。     

搜索引擎的工作原理和发展趋势

搜索引擎是一种能够通过Intemet接受用户的查询指令，并向用户提供符合其查询要求的信息资源网址的系统；是网络信息检索的首选工具。目前，各类搜索引擎层出不穷，市场发展需求巨大。因此对搜索引擎技术的研究是很有意义的。本文着重分析了搜索引擎的工作原理以及目前搜索引擎存在的问题，并为解决相关问题提出发展新的搜索引擎模式的建议。     

基于用户反馈的搜索引擎选择及结果归并

提出一种基于用户反馈的元搜索引擎模型，通过分析用户对搜索结果的评价，利用用户的反馈信息，分别定义搜索引擎质量分值和用户评价分值。给出一种依据搜索引擎质量分值进行的搜索引擎选择策略，使元搜索引擎可以针对不同查询关键字选择准确度高的搜索引擎进行查询。设计一种主客观影响因子结合的元搜索结果归并方法，使准确率高的搜索结果先返回给用户。     

搜索引擎自动性能评价系统构建的研究

检索系统的评价问题一直是信息领域最核心的问题之一。公开有效的搜索引擎质量评估可以指导搜索引擎用户挑选最有利于获取信息的手段，促进搜索引擎服务提供者与研究人员不断尝试新技术，为搜索引擎广告商挑选最有效的广告投放手段。传统的搜索引擎评价系统已经不能满足实际需要，提高搜索引擎评价方法的自动化程度势在必行。本文提出利用用户群体行为分析来进行搜索引擎自动性能评价，通过利用用户点击行为中蕴含的信息对检索结果进行相关性的评价，最终达到对各种不同搜索引擎进行性能评价的目的。     

MicroFtp搜索引擎的算法分析及功能比较

介绍了当前搜索引擎的分类、现状及发展，剖析了搜索引擎采用的核心技术——全文检索与分词技术，提出了MicroFtp新算法和基本原理，并将该算法与其他搜索引擎作了比较。     

基于后向链接的查询路由研究

尽管通用搜索引擎已经被广泛使用，但它们筛选用户查询结果中无关结果的功能一直不能任人满意，因此有些搜索需要使用专题搜索引擎，文章提出了一种基于中文专题搜索引擎的查询路由架构，该架构为用户的查询寻找合适的专题搜索引擎路由，并找出最佳搜索引擎，除了描述构架外，其中使用到的查询扩展和聚类算法文章也一并给出。     

利用智能搜索引擎构建网上知识平台

简单介绍了传统搜索引擎的工作原理及其分类，分析了智能搜索引擎的体系结构及其特点。讨论了构建网上知识平台的三个阶段，并提出利用智能搜索引擎来构建网上知识平台，为用户提供网上知识服务。     

集成搜索引擎的文本数据库选择

用户需要检索的信息往往分散存储在多个搜索多个搜索引擎各自的数据库里,对普通用户而言,访问多个搜索引擎并从返回的结果中分辨出确实有网页是一件费时费力的工作,集成搜索引擎则可以提供给用户一个同时记问多个搜索引擎人集成环境,集成搜索引擎能将其接收到的用户查询提交给底层的多个搜索引擎进行搜索,作为一种搜索工具,集成搜索引擎具有如WEB查询覆盖面比传统引擎更大,引警有更好的可扩展性等优点,讨论了解决集成搜索引擎的数据库选择问题的多种技术,针对用户提交的查询要求,通过数据库选择可以选定最有可能返回有用信息的底层搜索引擎。     

ISeeker--一个高效的元搜索引擎

介绍了一个高效的元搜索引擎系统ISeeker,提出了一套全面的搜索引擎评价和选择算法,在对检索结果进行融合处理时尽可能选择最好的结果,而且在用户察看结果时进行在线学习和调整。     

Towards a multimedia World-Wide Web information retrieval engine

Search engines are useful because they allow the user to find information of interest from the World-Wide Web. However, most of the popular search engines today are textual; they do not allow the user to find images from the Web. This paper describes a search engine that integrates text and image search. One or more Web sites can be indexed for both textual and image information, allowing the user to search based on keywords or images or both. Another problem with the current search engines is that they show the results as pages of scrolled list; this is not very user-friendly. Therefore our search engine allows the user to visualize the results in various ways. This paper explains the indexing and searching techniques of the search engine and highlights several features of the querying interface to make the retrieval process more efficient. Examples are used to show the usefulness of the technology.     

Search personalization through query and page topical analysis

Thousands of users issue keyword queries to the Web search engines to find information on a number of topics. Since the users may have diverse backgrounds and may have different expectations for a given query, some search engines try to personalize their results to better match the overall interests of an individual user. This task involves two great challenges. First the search engines need to be able to effectively identify the user interests and build a profile for every individual user. Second, once such a profile is available, the search engines need to rank the results in a way that matches the interests of a given user. In this article, we present our work towards a personalized Web search engine and we discuss how we addressed each of these challenges. Since users are typically not willing to provide information on their personal preferences, for the first challenge, we attempt to determine such preferences by examining the click history of each user. In particular, we leverage a topical ontology for estimating a user’s topic preferences based on her past searches, i.e. previously issued queries and pages visited for those queries. We then explore the semantic similarity between the user’s current query and the query-matching pages, in order to identify the user’s current topic preference. For the second challenge, we have developed a ranking function that uses the learned past and current topic preferences in order to rank the search results to better match the preferences of a given user. Our experimental evaluation on the Google query-stream of human subjects over a period of 1 month shows that user preferences can be learned accurately through the use of our topical ontology and that our ranking function which takes into account the learned user preferences yields significant improvements in the quality of the search results.     

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.     

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.     

基于相关术语集的搜索引擎选择

1 引言 Web从1991年出现以来,已经发展成为一个巨大的全球化信息空间,而且其信息容量仍在以指数形式飞速增长。面对海量Web信息资源,如何有效地检索Web信息,以帮助用户从大量文档信息集合中找到对给定查询请求有用的文档子集,也就成为一项重要而迫切的研究课题。     

基于搜索历史的用户兴趣模型的研究

提出了一种新的基于搜索历史的用户兴趣模型,目的是解决现有搜索引擎很难考虑用户兴趣来实现用户个性化搜索以及用户兴趣很难更新的问题。提出了基于搜索历史的用户兴趣的表达方法和自动隐式学习算法。全面地描述了用户兴趣模型的建立及通过自动隐式学习算法不断更新、优化模型的处理过程,并给出了对模型的评价标准。