基于本体的智能Web服务

一、引言随着Internet应用的普及和软件技术的进步,基于Web的网络计算模式或Internet计算模式得到了广泛的重视和应用。目前,面向Web应用的三层结构(或多层结构)、构件化的商务逻辑封装、基于XML和Web Services的自动电子商务等理论和技术深入人心。但创新是无止尽的。实际上,现有的这些Web技术并不能真正解决人们期待已久的智能搜索引擎、智能信息代理、智能交易代理等基于Web的个性化、智能化的服务。而这正是IWS(Intelligent Web Services)要解决的问题。表1总结了Web技术的发展和IWS在其中的位置。     

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．     

语义Web及其应用

语言文Web是下一代Interuet的发展方向。语义Web的定义、分层结构进行了概述，详细总结和研究了语义Web在Web服务、P2P网络、知识管理、E-learning、智能信息检索和语义Web挖掘、网格计算等多个领域的应用。     

智能服务——Web服务和语义Web集成研究

现有Web主要是为人使用Web上的内容设计,如何自动发现、执行、组合及互操作Web上的信息,提供智能服务,是Web服务努力的方向,Web服务虽然也制定了发现、描述、信息交换的标准,但这些标准不支持智能服务,其原因是现有Web服务缺乏清楚的语义标记,该文提出将语义Web技术和Web服务结合来实现智能服务的思想,即为Web服务增加语义标记以实现Web服务的智能性,并就其可行性进行了阐述,最后给出了具体实现的方法。     

Web计算：针对Web资源的网络计算

随着Web技术的不断发展和普及,它已经从最初的人类社会的辅助设施逐渐形成为一个与现实世界相对应的Web世界（Web-wide World）,作为现实世界的映像,与现实世界紧密的融合在一起.因此,Web计算已经成为推动并主导人类社会发展的越来越重要的因素.文章通过研究和分析Web的发展以及Web和Grid相互融合的趋势两大背景,将Web计算定位为一种Web资源的组织、管理和使用的分布式模型和技术.然后,分析了Web计算所具有的特点,如普遍性、连通性、位置透明性等;进一步,总结了Web计算研究所必须面临的一些任务,包括资源标识与定位、资源表示、资源传输以及信任管理.其中,Web世界的信任管理研究,将是Web计算研究的一个重要的方向和趋势,其目的就是为了提供一个诚信、合作的信任Web（Web of Trust）.     

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。     

CORBA/Web模式及其在智能舰船中的应用

针对传统的基于Web计算方式的不足，介绍了分布对象技术和Web相结合的体系结构；特别指出了CORBA／Web模式的优点，讨论了Java作为CORBA的Web使能技术；并应用到智能舰船的信息集成体系结构中，给出了简单的应用实例。     

一种混合搜索算法在智能Web中的应用

一种好的智能搜索算法对智能Web应用是非常重要的。为了在智能Web应用中实现快速智能搜索且能有效地去除垃圾信息,首先介绍了Lucene开源系统,详细分析了Lucene的系统结构以及PageRank算法。按照Lucene的框架规范,将Lucene很好地嵌入到自己的搜索引擎中,利用爬虫从互联网上收集数据,使用目前流行的Lucene和PageRank搜索技术在收集的数据上进行了实例研究。研究表明若在Lucene搜索中添加了PageRank分数,进行混合搜索排序时,相关性高的网页就会排到前面,从而有效提高在智能Web中搜索的准确率及效率。     

一种混合搜索算法在智能Web中的应用

一种好的智能搜索算法对智能Web应用是非常重要的。为了在智能Web应用中实现快速智能搜索且能有效地去除垃圾信息,首先介绍了Lucene开源系统,详细分析了Lucene的系统结构以及PageRank算法。按照Lucene的框架规范,将Lucene很好地嵌入到自己的搜索引擎中,利用爬虫从互联网上收集数据,使用目前流行的Lucene和PageRank搜索技术在收集的数据上进行了实例研究。研究表明若在Lucene搜索中添加PageRank分数,进行混合搜索排序时,相关性高的网页就会排到前面,从而有效提高在智能Web中搜索的准确率及效率。     

有上下文觉察能力的智能会议室系统模型的研究

通过利用多Agent、语义Web、上下文觉察和逻辑推理等技术，参考早期的普适计算系统，提出了一个新的智能会议室系统模型。该模型使用本体Web语言——OWL实现知识共享和上下文推理；使用逻辑推理检测和解决上下文知识的不一致问题，并向使用者提供一种策略语言来控制他们的私人信息。     

Computationally intelligent agents in economics and finance

This paper is an editorial guide for the second special issue on Computational Intelligence in economics and finance, which is a continuation of the special issue of Information Sciences, Vol. 170, No. 1. This second issue appears as a part of the outcome from the 3rd International Workshop on Computational Intelligence in Economics and Finance, which was held in Cary, North Carolina, September 26-30, 2003. This paper offers some main highlights of this event, with a particular emphasis on some of the observed progress made in this research field, and a brief introduction to the papers included in this special issue.     

基于Web企业竞争对手情报自动搜集平台

从互联网中准确有效及时地自动搜索出需要的信息,是Web信息处理中的一个重要研究课题。本文在所提出的基于搜索路径Web网页搜索和基于多知识网页信息抽取方法基础上,给出基于Web企业竞争对手情报自动收集平台的实现方法,该平台可以有效地从多个企业门户网站中,自动搜索出所需要的目标网页,并能够从目标网页中自动抽取其中多记录信息。本文利用该平台进行了企业人才招聘信息的自动搜索实验。实验结果证实了该平台在信息自动搜集方面的有效性和准确性。     

软计算方法——人工智能发展的新思路

静态的、严格的“物理符号系统”假设曾使传统人工智能一度陷人困境,软计算方法处理问题的灵活性和不确定性为人工智能的发展提供了新思路,作为一种创建计算智能的新颖方法,软计算对于人工智能的发展具有重要的意义。     

名正才能言顺:评《设计和制造中的计算智能》

该文应John Wiley&Sons出版社之邀简评了Kusiak教授的新著《设计和制造中的计 算智能》.针对什么是计算智能,书名与书内容的关系发表了作者的见解.     

Exploring spatial data through computational intelligence: a joint perspective

The dramatic increase in geospatial data occasioned by developments in digital mapping, remote sensing, IT, and widespread generalization of Geographic Information Systems (GIS), emphasises the importance of exploring new approaches to spatial analysis and modelling. This favours the creation of new knowledge and eventually helps the process of scientific discovery. In this context the special nature of spatial data is particularly relevant and should be taken into account (e.g. observations are not independent and data uncertainty and errors are often spatially structured). The tolerance of imprecision and uncertainty makes soft computing a potentially very useful tool in the GIS environment. Computational Intelligence (or Soft computing) fits particularly well with GIS applications in those cases where computationally hard problems cannot be solved by classical algorithmic approaches.     

ON KNOWLEDGE GRID AND GRID INTELLIGENCE: A SURVEY

The next generation Web Intelligence (WI) aims at enabling users to go beyond the existing online information search and knowledge queries functionalities and to gain, from the Web, practical wisdom for problem solving. To support such a Wisdom Web, we envision that a grid-like computing infrastructure with intelligent service agencies is needed, where these agencies can interact, self-organize, learn, and evolve their course of actions, identities, and interrelationships for new knowledge creation, as well as scientific and social evolution. In this paper, we first provide an overview of recent development in WI and Semantic/Knowledge Grid. Then, the fundamental capabilities of the Wisdom Web as well as the conceptual architecture of an intelligent Grid for supporting it are described. Technical challenges for realizing Grid Intelligence are highlighted and the recent advancements in related research areas are reviewed.     

Republication: On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach

Agent-based Methodology (ABM) is becoming indispensable for the interdisciplinary study of social and economic complex adaptive systems. The essence of ABM lies in the notion of autonomous agents whose behavior may evolve endogenously and can generate and mimic the corresponding complex system dynamics that the ABM is studying. Over the past decade, many Computational Intelligence (CI) methods have been applied to the design of autonomous agents, in particular, their adaptive schemes. This design issue is non-trivial since the chosen adaptive schemes usually have a profound impact on the generated system dynamics. Robert Lucas, one of the most influential modern economic theorists, has suggested using laboratories with human agents, also known as Experimental Economics, to help solve the selection issue. While this is a promising approach, laboratories used in the current experimental economics are not computationally equipped to meet the demands of the selection task. This paper attempts to materialize Lucas’ suggestion by establishing a laboratory where human subjects are equipped with the computational power that satisfies the computational equivalence condition. This is a Republication article. The online version of the original article can be found at .     

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.