个性化网络信息智能检索系统设计

本文结合现有的智能技术，提出了一个个性化网络信息智能检索模型。该模型能够对用户行为进行学习，从而建立一个反应用户兴趣的用户模型。而针对网络分布式的特点建立的检索代理模型，则能够通过对大量用户数据的分析积累丰富的知识。通过用户模型与检索代理模型的交互过程，向用户提供满意的检索结果。     

基于概念网的智能信息服务实现

采用基于均值和方差的文档分类。本文建立了一种新型概念网络模型。在此基础上。采用智能检索接口。建立了基于概念网的智能信息服务系统。系统依靠后台的概念网络。处理信息的收集和检索请求。将文档进行自动分类和关联。从而有效、合理的组织文档。在接收到用户的查询请求时。系统通过对用户查询意图的理解。过滤与用户查询意图不相关的文档。从而有效提高查询精度。试验表明。该系统能大大提高用户的检索效率和准确度。     

基金项目评审管理中智能交互式文档检索

讨论了非结构化文档信息的检索模型,分析了传统交互式信息检索方法,提出了面向基金项目文档查询的智能交互式信息检索过程和处理流程。基于用户评价项目文档的反馈信息,采用ID3算法、CLCC算法和SVM分类函数分别学习用户查询的潜在意图和目标,并应用所学习的规则知识或分类函数支持项目文档查询。以某基金评审管理中项目文档的查询为例进行了实验计算与分析。     

基于内容的图像检索的相关性反馈机制

介绍基于内容的图像检索技术，阐述了一种新的相关性反馈机制，它通过对用户指定的相关图像及不相关图像的特征分布进行统计分析来动态更新相似性度量和查询，从而更准确地表达用户特定的信息需求，提高了检索系统的性能。     

基于用户反馈的查询扩展研究

文章在分析传统查询扩展方法不足的基础上,提出了基于用户反馈的查询扩展方法,结合WordNet和概念相似性方法为用户提供用于查询扩展的候选集,最后用户从中挑选出扩展检索词用于查询扩展,从而为用户返回更加准确的检索结果.     

试论搜索引擎的技术改进

作为当前网络信息资源的重要检索工具 ,搜索引擎问世以来已得到广泛使用。作为一个新生事物 ,搜索引擎技术得到了快速发展 ,但同时也在广大网络用户的频繁使用中 ,逐渐暴露出其存在的各种各样的问题。本文主要从 3个不同方面探讨对搜索引擎进行技术改进的方法与思路。1　搜索引擎工作模式的改变众所周知 ,Ｗｅｂ最初是作为欧洲粒子物理研究中心(ＣＥＲＮ)内部的协作环境而诞生的 ,其分布式协作特性已成为全球信息共享的基础。但值得注意的是 ,基于Ｗｅｂ的一些信息应用系统采用的却是传统的集中式处理模式 ,以致这种集中式处理与Ｗｅｂ的分…     

基于Web使用挖掘的用户个性化服务研究

万维网是一个巨大的全球性的信息服务中心。随着诸如新闻、广告、消费信息、金融管理、远程教育、政府网站、电子商务等的日益普及 ,提供网络信息服务的竞争日益激烈。谁能更方便地为用户提供所需要的网络资源 ;谁能提供更贴近用户的个性化服务 ;谁能更快捷抓住用户新的需求 ,是能否成功为用户提供网络业务的关键。现代社会的竞争趋势要求对因特网上大量出现和产生的信息进行实时和深层次的分析 ,虽然借助于强大的搜索引擎和搜索技术 ,用户仍然在分析和使用这些信息时面临许多困难。同时基于ＷＷＷ的Ｗｅｂ站点设计、Ｗｅｂ服务设计、Ｗｅｂ…     

多Agent协作的智能搜索引擎系统的设计与实现

随着网络信息资源的迅速增加和实时更新,如何及时准确地获取所需的有用信息是现代网络信息检索技术需要解决的主要问题。针对现有搜索引擎的不足,设计了一种基于多Agent协作的智能搜索引擎系统模型,结合人工智能的Agent技术与元搜索引擎技术来共同执行检索任务,实现基于用户兴趣的智能化与个性化。提高查询的精度和速度．保证了查全率。并对该系统实现的关键技术作了介绍。     

因特网上信息索引数据库的利用

网络信息资源搜索引擎的第二功能模块为信息检索与提供。本文讨论了检索模块的工作原理与查询过程。通过HTML浏览程序,在SX+AF+MX索引数据库中检索,快速反馈检索结果。并分组编辑输出命中网点的索引信息,阅读索引后若有进一步的需求．可点击索引记录的URL,链接出网点的原始信息资源,提供用户使用。     

基于Web数据挖掘的个性化信息智能Agent挖掘系统模型

个性化智能挖掘是近几年出现的一个崭新的研究方向,它是人工智能与数据挖掘技术在Web或Internet环境下相互融合的产物。大部分个性化信息挖掘都只是建立在纯粹的Web数据挖掘之上,然而面对大部分的智能化技术的出现,面对用户能够快速准确地检索自己最想要的信息的需求,Web数据挖掘要进行相应的扩展,通过将Web数据挖掘技术和智能Agent技术相结合,从而满足用户的需求。本文主要提出两个模型:典型的个性化Web挖掘模型和个性化Agent智能挖掘模型。     

相关反馈技术在知识检索中的应用

本文从相关反馈技术的基本原理出发，综合应用信息管理与机器学习，探讨了相关反馈技术在知识检索中的应用模式、相关反馈检索算法和相关反馈学习算法，最后提出了对该领域研究的建议。     

Enhancing query translation with relevance feedback in translingual information retrieval

As an effective technique for improving retrieval effectiveness, relevance feedback (RF) has been widely studied in both monolingual and translingual information retrieval (TLIR). The studies of RF in TLIR have been focused on query expansion (QE), in which queries are reformulated before and/or after they are translated. However, RF in TLIR actually not only can help select better query terms, but also can enhance query translation by adjusting translation probabilities and even resolving some out-of-vocabulary terms. In this paper, we propose a novel relevance feedback method called translation enhancement (TE), which uses the extracted translation relationships from relevant documents to revise the translation probabilities of query terms and to identify extra available translation alternatives so that the translated queries are more tuned to the current search. We studied TE using pseudo-relevance feedback (PRF) and interactive relevance feedback (IRF). Our results show that TE can significantly improve TLIR with both types of relevance feedback methods, and that the improvement is comparable to that of query expansion. More importantly, the effects of translation enhancement and query expansion are complementary. Their integration can produce further improvement, and makes TLIR more robust for a variety of queries.     

Query expansion and dimensionality reduction: Notions of optimality in Rocchio relevance feedback and latent semantic indexing

Rocchio relevance feedback and latent semantic indexing (LSI) are well-known extensions of the vector space model for information retrieval (IR). This paper analyzes the statistical relationship between these extensions. The analysis focuses on each method’s basis in least-squares optimization. Noting that LSI and Rocchio relevance feedback both alter the vector space model in a way that is in some sense least-squares optimal, we ask: what is the relationship between LSI’s and Rocchio’s notions of optimality? What does this relationship imply for IR? Using an analytical approach, we argue that Rocchio relevance feedback is optimal if we understand retrieval as a simplified classification problem. On the other hand, LSI’s motivation comes to the fore if we understand it as a biased regression technique, where projection onto a low-dimensional orthogonal subspace of the documents reduces model variance.     

基于Web日志挖掘的网络动态竞争情报分析研究

通过挖掘蕴含在WebE1志中的隐含模式和知识,Web日志挖掘为企业实现网络竞争情报动态分析提供了一种有效的途径。文章分析Web日志挖掘的原理和过程,并探讨Web日志挖掘在动态竞争情报分析中的应用。     

基于机器学习的图像检索机制的研究

主要针对当前基于低水平特征的图像检索机制不能捕获图像语义的状况 ,讨论了使用长期学习的方法来学习用户的相关反馈 ,以此推断语义 ,构造语义空间 ,并结合短期学习方法 ,通过运用学习监督机制来推断用户信息需求 ,优化查询 ,逐步提高搜索引擎的性能     

Using Web structure and summarisation techniques for Web content mining

The dynamic nature and size of the Internet can result in difficulty finding relevant information. Most users typically express their information need via short queries to search engines and they often have to physically sift through the search results based on relevance ranking set by the search engines, making the process of relevance judgement time-consuming. In this paper, we describe a novel representation technique which makes use of the Web structure together with summarisation techniques to better represent knowledge in actual Web Documents. We named the proposed technique as Semantic Virtual Document (SVD). We will discuss how the proposed SVD can be used together with a suitable clustering algorithm to achieve an automatic content-based categorization of similar Web Documents. The auto-categorization facility as well as a “Tree-like” Graphical User Interface (GUI) for post-retrieval document browsing enhances the relevance judgement process for Internet users. Furthermore, we will introduce how our cluster-biased automatic query expansion technique can be used to overcome the ambiguity of short queries typically given by users. We will outline our experimental design to evaluate the effectiveness of the proposed SVD for representation and present a prototype called iSEARCH (Intelligent SEarch And Review of Cluster Hierarchy) for Web content mining. Our results confirm, quantify and extend previous research using Web structure and summarisation techniques, introducing novel techniques for knowledge representation to enhance Web content mining.     

Relevance feedback and cross-language information retrieval

This paper presents a study of relevance feedback in a cross-language information retrieval environment. We have performed an experiment in which Portuguese speakers are asked to judge the relevance of English documents; documents hand-translated to Portuguese and documents automatically translated to Portuguese. The goals of the experiment were to answer two questions (i) how well can native Portuguese searchers recognise relevant documents written in English, compared to documents that are hand translated and automatically translated to Portuguese; and (ii) what is the impact of misjudged documents on the performance improvement that can be achieved by relevance feedback. Surprisingly, the results show that machine translation is as effective as hand translation in aiding users to assess relevance in the experiment. In addition, the impact of misjudged documents on the performance of RF is overall just moderate, and varies greatly for different query topics.     

Combining the evidence of different relevance feedback methods for information retrieval

It has been known that retrieval effectiveness can be significantly improved by combining multiple evidence from different query or document representations, or multiple retrieval techniques. In this paper, we combine multiple evidence from different relevance feedback methods, and investigate various aspects of the combination. We first generate multiple query vectors for a given information problem in a fully automatic way by expanding an initial query vector with various relevance feedback methods. We then perform retrieval runs for the multiple query vectors, and combine the retrieval results. Experimental results show that combining the evidence of different relevance feedback methods can lead to substantial improvements of retrieval effectiveness.     

Web信息检索系统中的网页质量分析方法评价

改进对高质量网页的检索精度,将会极大提高Web信息检索系统的用户满意度。首先提出了信息检索中的“有用性”指标,并据此论述了基于网页质量分析方法的Web信息检索模型,然后提出了网页质量直接测度指标和网页质量间接测度指标。最后,详细介绍了各种网页质量指标的相关研究内容和方法,并做出了针对性的评价。     

A test of genetic algorithms in relevance feedback

There have been recent applications of genetic algorithms to information retrieval, mostly with respect to relevance feedback. Nevertheless, they are yet to be evaluated in a way that allows them to be compared with each other and with other relevance feedback techniques. We here implement the different genetic algorithms that have been applied in the literature together with some of our own variations, and evaluate them using the residual collection method described by Salton in 1990 for the evaluation of relevance feedback techniques. We compare the results with those of the Ide dec-hi method, which is one of the traditional methods that yields the best results.