基于多知识的Web网页信息抽取方法

从Web网页中自动抽取所需要的信息内容,是互联网信息智能搜取的一个重要研究课题,为有效解决网页信息抽取所需的信息描述知识获取问题,这里提出了一个种基于多知识的Web网页信息抽取方法（简称MKIE方法）。该方法将网页信息抽取所需的知识分为二类,一类是描绘网页内容本身表示特点,以及识别各网信息对象的确定模式知识,另一类则描述网页信息记录块,以及各网页信息对象的非确定模式知识,MKIE方法根据前一类知识,动态分析获得后一类知识;并利用这两类知识,最终完全从信息内容类似担其表现形式各异的网页中,抽取出所需要的信息,美大学教员论文网页信息抽取实验结果表明,MKIE方法具有较强的网而信息自动识别与抽取能力。     

A novel feature selection framework for automatic web page classification

The number of Internet users and the number of web pages being added to WWW increase dramatically every day.It is therefore required to automatically and e?ciently classify web pages into web directories.This helps the search engines to provide users with relevant and quick retrieval results.As web pages are represented by thousands of features,feature selection helps the web page classifiers to resolve this large scale dimensionality problem.This paper proposes a new feature selection method using Ward s minimum variance measure.This measure is first used to identify clusters of redundant features in a web page.In each cluster,the best representative features are retained and the others are eliminated.Removing such redundant features helps in minimizing the resource utilization during classification.The proposed method of feature selection is compared with other common feature selection methods.Experiments done on a benchmark data set,namely WebKB show that the proposed method performs better than most of the other feature selection methods in terms of reducing the number of features and the classifier modeling time.     

WWW缓冲挖掘中基于粗糙集的用户模糊兴趣建模

如何有效充分地利用WWW缓冲中的信息,其关键是建立一个合适的用户兴趣模型和构造合适的兴趣挖掘算法。用户的兴趣是一个相对模糊的概念,简单兴趣模型通过（词条,权重）来刻画兴趣,但是它的表示粒度太小,不能贴切地描述用户的兴趣。文中在充分分析WWW缓冲模型的基础上提出了基于粗糙集的用户模糊兴趣概念。利用该方法可以对WWW缓冲中的网页文档和文档集合进行统一建模,这为利用用户历史兴趣信息和进行兴趣匹配提供了便利。     

WWW网页布局规则初探

连入WWW(WorldWideWeb)的用户和网点数量每年成倍增长.要想使一个网点在数以百万计的网点中给访问者留下深刻的印象,设计出一个含有丰富信息、易于浏览、视觉上怡人的网页(WebPages)是不可或缺的.本文从Gestalt心理学、传统印刷术、超文本制作和人机交互学出发,尝试提出一个网络文档(WebDocuments)界面布局的设计规则,包括文本、图形、静态和动态的网络文档.本文把网络文档分成五种基本类型,然后给出了一些布局规则.     

一种计算求精网页主题抽取结果的方法

传统的主题抽取方法单纯依靠分析网页内容的来自动获取网页主题，其分析结果并不十分精确．在WWW上，网页之间通过超链接来互相联系，而链接关系紧密的网页趋向于属于同一主题、基于这一思想，本文提出了一种利用Web链接结构信息来对主题抽取结果进行求精的方法，其通过所链接网页对本网页的影响来修正本网页的主题权值．本文还通过一个实际应用例子，分析了这一方法的特点。     

一种WWW搜索引擎的设计与实现

随着Internet在我国的迅速发展和WWW信息的不断增长,迫切需要开发中英文兼容的WWW搜索引擎来获得所需的信息。该文在分析WWW搜索引擎主要功能模块:信息采集模块、信息预处理模块和信息查询模块的基础上,提出采用人工智能搜索算法来遍历网页,对中英文网页进行自动的索引,并用向量空间的表示方法来表示网页内容和用户输入的查询表达式。实践证明,使用该搜索引擎,可以快速准确地搜索到用户所需的信息。     

Two-level Clustering of Web Sites Using Self-Organizing Maps

Web sites contain an ever increasing amount of information within their pages. As the amount of information increases so does the complexity of the structure of the web site. Consequently it has become difficult for visitors to find the information relevant to their needs. To overcome this problem various clustering methods have been proposed to cluster data in an effort to help visitors find the relevant information. These clustering methods have typically focused either on the content or the context of the web pages. In this paper we are proposing a method based on Kohonen’s self-organizing map (SOM) that utilizes both content and context mining clustering techniques to help visitors identify relevant information quicker. The input of the content mining is the set of web pages of the web site whereas the source of the context mining is the access-logs of the web site. SOM can be used to identify clusters of web sessions with similar context and also clusters of web pages with similar content. It can also provide means of visualizing the outcome of this processing. In this paper we show how this two-level clustering can help visitors identify the relevant information faster. This procedure has been tested to the access-logs and web pages of the Department of Informatics and Telecommunications of the University of Athens.     

面向分层结构的网页分类与抓取

传统网络爬虫为基于关键字检索的通用搜索引擎服务,无法抓取网页类别信息,给文本聚类和话题检测带来计算效率和准确度问题。本文提出基于站点分层结构的网页分类与抽取,通过构建虚拟站点层次分类树并抽取真实站点分层结构,设计并实现了面向分层结构的网页抓取;对于无分类信息的站点,给出了基于标题的网页分类技术,包括领域知识库构建和基于《知网》的词语语义相似度计算。实验结果表明,该方法具有良好的分类效果。     

基于Ontology的网页关系识别研究

对万维网上大量的网页之间进行特定语义关系的分析,将这样的工作用于搜索引擎中,可以实现智能化的查询,和提供其它个性化服务。本文借助于Ontology中的关系实例,在网页分类的基础上对网页之间的关系进行自动识别,同时提出了网页关系识别规则的自动生成和优化方法。     

Analysis of navigation behaviour in web sites integrating multiple information systems

Abstract. The analysis of web usage has mostly focused on sites composed of conventional static pages. However, huge amounts of information available in the web come from databases or other data collections and are presented to the users in the form of dynamically generated pages. The query interfaces of such sites allow the specification of many search criteria. Their generated results support navigation to pages of results combining cross-linked data from many sources. For the analysis of visitor navigation behaviour in such web sites, we propose the web usage miner (WUM), which discovers navigation patterns subject to advanced statistical and structural constraints. Since our objective is the discovery of interesting navigation patterns, we do not focus on accesses to individual pages. Instead, we construct conceptual hierarchies that reflect the query capabilities used in the production of those pages. Our experiments with a real web site that integrates data from multiple databases, the German SchulWeb, demonstrate the appropriateness of WUM in discovering navigation patterns and show how those discoveries can help in assessing and improving the quality of the site. Received June 21, 1999 / Accepted December 24, 1999     

A text mining approach for automatic construction of hypertexts

The research on automatic hypertext construction emerges rapidly in the last decade because there exists a urgent need to translate the gigantic amount of legacy documents into web pages. Unlike traditional ‘flat’ texts, a hypertext contains a number of navigational hyperlinks that point to some related hypertexts or locations of the same hypertext. Traditionally, these hyperlinks were constructed by the creators of the web pages with or without the help of some authoring tools. However, the gigantic amount of documents produced each day prevent from such manual construction. Thus an automatic hypertext construction method is necessary for content providers to efficiently produce adequate information that can be used by web surfers. Although most of the web pages contain a number of non-textual data such as images, sounds, and video clips, text data still contribute the major part of information about the pages. Therefore, it is not surprising that most of automatic hypertext construction methods inherit from traditional information retrieval research. In this work, we will propose a new automatic hypertext construction method based on a text mining approach. Our method applies the self-organizing map algorithm to cluster some at text documents in a training corpus and generate two maps. We then use these maps to identify the sources and destinations of some important hyperlinks within these training documents. The constructed hyperlinks are then inserted into the training documents to translate them into hypertext form. Such translated documents will form the new corpus. Incoming documents can also be translated into hypertext form and added to the corpus through the same approach. Our method had been tested on a set of at text documents collected from a newswire site. Although we only use Chinese text documents, our approach can be applied to any documents that can be transformed to a set of index terms.     

基于内容模块化的网页快速浏览技术

在万维网(WorldWideWeb)庞大的信息海洋中,如何提高信息检索和浏览效率,一直是重要的研究内容之一。文章中提出了将一复杂网页的内容按照模块化方式进行组织和使用的新思想,通过对HTML语言的简单扩充和对浏览器与Web服务器的相应改进,新浏览器可使用户有效避免对无关网页的浏览和减少网页中无关信息的传输,明显提高了网页浏览速度,尤其是对拨号上网用户,其效果更为明显。     

数据挖掘技术在Web预取中的应用研究

WWW以其多媒体的传输及良好的交互性而倍受青睐,虽然近几年来网络速度得到了很大的提高,但是由于接入Internet的用户数量剧增以及Web服务和网络固有的延迟,使得网络越来越拥护,用户的服务质量得不到很好的保证。为此文中提出了一种智能Web预取技术,它能够加快用户浏览Web页面时获取页面的速度。该技术通过简化的WWW数据模型表示用户浏览器缓冲器中的数据,在此基础上利用数据挖掘技术挖掘用户的兴趣关联规则,存放在兴趣关联知识库中,作为对用户行为进行预测的依据。在用户端,智能代理负责用户兴趣的挖掘及基于兴趣关联知识库的Web预取,从而对用户实现透明的浏览器加速。     

Improving search results with data mining in a thematic search engine

The problem of obtaining relevant results in web searching has been tackled with several approaches. Although very effective techniques are currently used by the most popular search engines when no a priori knowledge on the user's desires beside the search keywords is available, in different settings it is conceivable to design search methods that operate on a thematic database of web pages that refer to a common body of knowledge or to specific sets of users. We have considered such premises to design and develop a search method that deploys data mining and optimization techniques to provide a more significant and restricted set of pages as the final result of a user search. We adopt a vectorization method based on search context and user profile to apply clustering techniques that are then refined by a specially designed genetic algorithm. In this paper we describe the method, its implementation, the algorithms applied, and discuss some experiments that has been run on test sets of web pages.     

Web站点的超链结构挖掘

ＷＷＷ是一个由成千上万个分布在世界各地的Ｗｅｂ站点组成的全球信息系统,每个Ｗｅｂ站点又是一个由许多Ｗｅｂ页构成的信息（子）系统。由于一个文档作者可以通过超链把自己的文档与任意一个已知的Ｗｅｂ页链接起来,而一个 Ｗｅｂ站点上的信息资源又通常是由许多人共同提供的, 因此 Ｗｅｂ站点内的超链链接通常是五花八门、各种各样的,它们可以有各种含义和用途。文章分析了ＷＷＷ系统中超链的使用特征和规律,提出了一个划分超链类型、挖掘站点结构的方法,初步探讨了它在信息收集和查询等方面的应用。     

Discovering stages in web navigation for problem-oriented navigation support

Users of web sites often do not know exactly which information they are looking for nor what the site has to offer. The purpose of their interaction is not only to fulfill but also to articulate their information needs. In these cases users need to pass through a series of pages before they can use the information that will eventually answer their questions. Current systems that support navigation predict which pages are interesting for the users on the basis of commonalities in the contents or the usage of the pages. They do not take into account the order in which the pages must be visited. In this paper we propose a method to automatically divide the pages of a web site on the basis of user logs into sets of pages that correspond to navigation stages. The method searches for an optimal number of stages and assigns each page to a stage. The stages can be used in combination with the pages’ topics to give better recommendations or to structure or adapt the site. The resulting navigation structures guide the users step by step through the site providing pages that do not only match the topic of the user’s search, but also the current stage of the navigation process.     

Automatically Determining Semantics for World Wide Web Multimedia Information Retrieval

Search engines are useful because they allow the user to find information of interest from the World Wide Web (WWW). However, most of the popular search engines today are textual; they do not allow the user to find images from the web. For effective retrieval, determining the semantics of the images is essential. In this paper, we describe the problems in determining the semantics of images on the WWW and the approach of AMORE, a WWW search engine that we have developed. AMORE's techniques can be extended to other media like audio and video. We explain how we assign keywords to the images based on HTML pages and the method to determine similar images based on the assigned text. We also discuss some statistics showing the effectiveness of our technique. Finally, we present the visual interface of AMORE with the help of several retrieval scenarios.     

Using the web infrastructure to preserve web pages

To date, most of the focus regarding digital preservation has been on replicating copies of the resources to be preserved from the “living web” and placing them in an archive for controlled curation. Once inside an archive, the resources are subject to careful processes of refreshing (making additional copies to new media) and migrating (conversion to new formats and applications). For small numbers of resources of known value, this is a practical and worthwhile approach to digital preservation. However, due to the infrastructure costs (storage, networks, machines) and more importantly the human management costs, this approach is unsuitable for web scale preservation. The result is that difficult decisions need to be made as to what is saved and what is not saved. We provide an overview of our ongoing research projects that focus on using the “web infrastructure” to provide preservation capabilities for web pages and examine the overlap these approaches have with the field of information retrieval. The common characteristic of the projects is they creatively employ the web infrastructure to provide shallow but broad preservation capability for all web pages. These approaches are not intended to replace conventional archiving approaches, but rather they focus on providing at least some form of archival capability for the mass of web pages that may prove to have value in the future. We characterize the preservation approaches by the level of effort required by the web administrator: web sites are reconstructed from the caches of search engines (“lazy preservation”); lexical signatures are used to find the same or similar pages elsewhere on the web (“just-in-time preservation”); resources are pushed to other sites using NNTP newsgroups and SMTP email attachments (“shared infrastructure preservation”); and an Apache module is used to provide OAI-PMH access to MPEG-21 DIDL representations of web pages (“web server enhanced preservation”).     

An XML-enabled data extraction toolkit for web sources

The amount of useful semi-structured data on the web continues to grow at a stunning pace. Often interesting web data are not in database systems but in HTML pages, XML pages, or text files. Data in these formats are not directly usable by standard SQL-like query processing engines that support sophisticated querying and reporting beyond keyword-based retrieval. Hence, the web users or applications need a smart way of extracting data from these web sources. One of the popular approaches is to write wrappers around the sources, either manually or with software assistance, to bring the web data within the reach of more sophisticated query tools and general mediator-based information integration systems. In this paper, we describe the methodology and the software development of an XML-enabled wrapper construction system—XWRAP for semi-automatic generation of wrapper programs. By XML-enabled we mean that the metadata about information content that are implicit in the original web pages will be extracted and encoded explicitly as XML tags in the wrapped documents. In addition, the query-based content filtering process is performed against the XML documents. The XWRAP wrapper generation framework has three distinct features. First, it explicitly separates tasks of building wrappers that are specific to a web source from the tasks that are repetitive for any source, and uses a component library to provide basic building blocks for wrapper programs. Second, it provides inductive learning algorithms that derive or discover wrapper patterns by reasoning about sample pages or sample specifications. Third and most importantly, we introduce and develop a two-phase code generation framework. The first phase utilizes an interactive interface facility to encode the source-specific metadata knowledge identified by individual wrapper developers as declarative information extraction rules. The second phase combines the information extraction rules generated at the first phase with the XWRAP component library to construct an executable wrapper program for the given web source.