Web搜索中的数据挖掘技术研究

WWW已经成为世界上是大的分布式信息系统,如何快速有效地搜索用户所需的资源一直是研究热点。Web挖掘也已经成为数据挖掘中相对成熟的一个分支。本文针对Web资源搜索中利用的相关Web挖掘技术做一个综述。文章首先对目前流行的Web内容挖掘方面的常用技术进行了研究分析,然后着重研究了Web结构挖掘技术,介绍并评价了多种算法模型。接着介绍了用户使用的挖掘,并提出了Web内容挖掘技术,结构挖掘技术和用户使用挖掘相结合,应用于开发智能型搜索引擎的趋势。     

Web挖掘技术在电子商务中的应用

随着互联网的高速发展,Web挖掘由于其独特的优点,在电子商务的应用中扮演了越来越重要的角色。文章主要介绍了Web挖掘的概念和分类,论述了电子商务中Web挖掘的过程和方法,最后阐述了Web挖掘在电子商务中的具体应用。     

Web挖掘技术在电子商务中的应用

随着互联网的高速发展，Web挖掘由于其独特的优点，在电子商务的应用中扮演了越来越重要的角色。文章主要介绍了web挖掘的概念和分类，论述了电子商务中Web挖掘的过程和方法，最后阐述了Web挖掘在电子商务中的具体应用。     

数据挖掘在Web智能化中应用研究

分析了Web信息的特点和目前开发利用的局限,提出在Web上采用数据挖掘技术即Web挖掘,促进web智能化的观点。全面阐述了Web挖掘在Web智能化中的几个重要应用。指出Web挖掘是Web技术中一个重要的研究领域,是发现蕴藏在web上知识、区分权威链接、理解用户访问模式和网页语义结构的关键,它使充分利用Web大量的真正有价值的信息成为可能,为智能化Web奠定了基础。     

Information gathering and searching approaches on the Web

The information accessible through the Internet is increasing explosively as the Web is getting more and more widespread. In this situation, the Web is indispensable information resource for both of information gathering and information searching. Though traditional information retrieval techniques have been applied to information gathering and searching in the Web, they are insufficient for this new form of information source. Fortunately some Al techniques can be straightforwardly applicable to such tasks in the Web, and many researchers are trying this approach. In this paper, we attempt to describe the current state of information gathering and searching technologies in the Web, and the application of AI techniques in the fields. Then we point out limitations of these traditional and AI approaches and introduce two aapproaches: navigation planning and a Mondou search engine for overcoming them. The navigation planning system tries to collect systematic knowledge, rather than Web pages, which are only pieces of knowledge. The Mondou search engine copes with the problems of the query expansion/modification based on the techniques of text/web mining and information visualization. Seiji Yamada, Dr. Eng.: He received the B.S., M.S. and Ph.S. degrees in control engineering and artificial intelligence from Osaka University, Osaka, Japan, in 1984, 1986 and 1989, respectively. From 1989 to 1991, he served as a Research Associate in the Department of Control Engineering at Osaka University. From 1991 to 1996, he served as a Lecturer in the Institute of Scientific and Industrial Research at Osaka University. In 1996, he joined the Department of Computational Intelligence and Systems Science at Tokyo Institute of Technology, Yokohama, Japan, as an Associate Professor. His research interests include artificial intelligence, planning, machine learning for a robotics, intelligent information retrieval in the WWW, human computer interaction, He is a member of AAAI, IEEE, JSAI, RSJ and IEICE. Hiroyuki Kawano, Dr.Eng.: He is an Associate Professor at the Department of Systems Science, Graduate School of Informatics, Kyoto University, Japan. He obtained his B.Eng. and M.Eng. degrees in Applied Mathematics and Physics, and his Dr.Eng. degree in Applied Systems Science from Kyoto University. His research interests are in advanced database technologies, such as data mining, data warehousing, knowledge discovery and web search engine (Mondou). He has served on the program committees of several conferences in the areas of Data Base Systems, and technical committes of advanced information systems.     

Web智能研究现状与发展趋势

Web智能是近年出现的一个崭新的研究方向,它是人工智能和高级信息技术在新的Web和Internet环境下相互融合的产物．首先从总体上讨论了Web智能的概念、研究内容和功能技术框架,然后分别就Web智能的几个核心方面的研究现状进行了综述,主要包括语义Web与ontology,Web Agent和Web挖掘等,并进一步给出了它们的研究重点和发展方向,最后是关于Web智能的研究展望和面临的挑战,指出智慧Web是Web智能研究的目标和中长期发展方向．     

Web日志挖掘中数据预处理方法的研究

Web日志挖掘是目前网上智能信息检索和电子商务的主要研究课题之一。而数据预处理在Web日志挖掘中起着很重要的作用，直接影响日志挖掘的质量和结果。介绍了Web日志挖掘数据预处理过程，综述了国际上的研究现状，及流行的处理方法。针对预处理步骤中的用户会话识别和路径填充进行了相应的改进。根据评估会话构造方法的标准，通过实验对给出的新方法与其他方法进行了分析比较。     

Web日志挖掘中数据预处理方法的研究

Web日志挖掘是目前网上智能信息检索和电子商务的主要研究课题之一。而数据预处理在Web日志挖掘中起着很重要的作用,直接影响日志挖掘的质量和结果。介绍了Web日志挖掘数据预处理过程,综述了国际上的研究现状,及流行的处理方法。针对预处理步骤中的用户会话识别和路径填充进行了相应的改进。根据评估会话构造方法的标准,通过实验对给出的新方法与其他方法进行了分析比较。     

Web数据挖掘技术及应用

Web数据挖掘是数据挖掘技术在Web信息集合上的应用，Web数据具有本身的特点，Web数据挖掘可以分为三类，各自有其相关技术，随着Internet的发展，Web数据挖掘有着越来越广泛的应用。     

基于Web挖掘与IIPP技术融合的IDSS研究

随着Internet的迅猛发展,面对信息的海量增长,本文分析了传统的智能决策支持系统,并简述了Web挖掘及其分类,结合智能信息推拉技术,提出了将Web挖掘与智能信息推拉技术融合的IDSS 的研究.     

Web数据挖掘在智能选课系统中的应用研究

Web数据挖掘技术是一种热门的信息技术,是数据挖掘技术在Web环境下的应用。文章首先阐述了Web数据挖掘技术的基本原理,接着构建了基于Web数据挖掘的学校智能选课系统模型,根据学生的不同的兴趣和特点,提供不同的课程选择,更有利于实现对学生的培养。     

On the role of trust in collaborative Web search

Recommender systems combine ideas from information retrieval, user modelling, and artificial intelligence to focus on the provision of more intelligent and proactive information services. As such, recommender systems play an important role when it comes to assisting the user during both routine and specialised information retrieval tasks. Like any good assistant it is important that users can trust in the ability of a recommender system to respond with timely and relevant suggestions. In this paper, we will look at a collaborative recommendation system operating in the domain of Web search. We will show how explicit models of trust can help to inform more reliable recommendations that translate into more relevant search results. Moreover, we demonstrate how the availability of this trust-model facilitates important interface enhancements that provide a means to declare the provenance of result recommendations in a way that will allow searchers to evaluate their likely relevance based on the reputation and trustworthiness of the recommendation partners behind these suggestions.     

The Internet, intranets, and the AI renaissance

Virtually cost-free publication on the World Wide Web has led to information overload. Artificial intelligence (AI), with its roots in knowledge representation, is experiencing a renaissance as new tools emerge to make the Web more tractable. Why do these Internet-based applications herald an AI renaissance? AI has come to play a crucial role in Information Age retrieval strategies. Internet-based applications can exploit a wide range of AI developments. In this survey, we look at examples of the following AI technologies: natural language processing (concept-based Internet searching); machine-learning (WebWatcher); heuristic rules for establishing preference (Letizia); rule-based/heuristic natural language processing (ContactFinder, FAQFinder, Globenet); and neural networks (Autonomy). This isn't AI for AI's sake-this renaissance is not one of stand-alone AI applications. Unlike first-generation AI applications, AI can now be embedded in heterogeneous networked computing environments and used for searching, retrieval and analysis of previously unimaginable quantities of data. Because the wealth of data makes direct human analysis impossible, AI-based support has become necessary to help users fully exploit that information. Our increasingly competitive and technology-driven world has reduced the time available to us for decision-making. To survive in this environment, we are increasingly turning to advanced computer technologies, such as intelligent agents, and delegating some of that decision-making to these electronic surrogates     

Semantic Web Mining: State of the art and future directions

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: More and more researchers are working on improving the results of Web Mining by exploiting semantic structures in the Web, and they make use of Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself.The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of these resources. Therefore, automated schemes for learning the relevant information are increasingly being used. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web sites and navigation behavior are becoming more and more common. Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.     

基于Web和数据挖掘技术的智能教学系统

随着ICAI系统本身和网络的应用与发展,现有的ICAI系统已不能充分发挥其智能性,无法实现对潜在的大量聚集的信息进行发现和利用,造成了资源的极大浪费,为此构建了一个基于Web和数据挖掘技术的智能教学系统的实用模型,并分析和讨论了实现的基本原理和关键技术。从而实现了学生和教师间的个性化教学,优化了系统的性能。     

一个自适应Web站点构架的设计

现今Web站点是越来越复杂而且不智能化。用户在访问Web站点时经常会碰到很多问题,主要原因是Web站点对用户的需求缺乏适应性。文中研究了自适应Web站点,提出一个理论框架,并针对此框架给出一个构建自适应Web站点的系统架构,介绍了使用文本挖掘方法和Web用法挖掘方法,改善Web站点的结构和组织形式以使站点达到更好的效果。主要通过挖掘Web服务器日志数据使站点更容易访问。     

Web挖掘在现代远程教育中的应用

从Web上异质的、非结构化的数据中发现有用的知识或者模式,是目前数据挖掘研究中的一个重要内容。Web挖掘就是从Web文档和Web活动中抽取感兴趣的、潜在的有用模式和隐藏的信息。文章介绍了Web挖掘基本情况。在此基础上对基于Web的文本挖掘进行了分析研究,给出了一个基于Web的文本挖掘的结构模型图。在Web挖掘和数据挖掘研究的基础上,提出了一个智能化、个性化的现代远程教育系统结构模型。它比传统的远程教育系统具有更大的发展前景。     

基于本体的Web分类技术研究

主要提出了一种基于本体的抽象的Web挖掘模型。首先利用本体的方法表示出要挖掘的领域,然后把从用户处收集来的数据转换成表格;最后再根据定义和公式来进行知识发现。抽象的Web挖掘模型可以提取出语义Web中隐藏在大量信息背后的近似概念,来实现知识发现。     

源于信息挖掘的新型智能化决策支持系统

阐述了以结构化数据和复杂类型数据挖掘为主要内容的信息挖掘技术。采用7库(模型库、综合知识库、数据库、方法库、文本库、日志库、多媒体库)与双网(Internet、Intranet)相结合的体系结构,以信息挖掘技术为核心,提出源于信息挖掘的新型智能化决策支持系统(IDSSIM)。旨在解决决策支持系统对半结构化数据、非结构化数据的挖掘处理能力,使之适应目前信息源的多样型和动态变化性的特点,提供更加丰富的决策信息。     

智能检索引擎中的网络数据挖掘技术优化研究

随着计算机网络技术的不断发展,对于Web Service检索技术的要求也越来越大。并且现在网络环境当中数据信息流量十分庞大,对于信息可以做到深入搜索,实现全方位信息查询是非常有必要的。为此,利用网络数据挖掘技术在智能检索引擎中的应用,以文本描述为信息作为本文的研究对象,为用户提供运用查询要求实现概念检索功能。其中强调在智能搜索引擎当中的网络数据挖掘技术进行优化研究,从结构设计以及算法分析上总结出当前网络数据挖掘应用智能检索的可能性。最终设计出一种利用数据挖掘技术的智能检索模型,实现在众多网络数据中可以准确快速的进行详细的信息检索功能。