补充反馈模块的深度标注框架研究

由数据库生成的动态Web页是静态页面的数百倍，直接针对Web页产生过程的深度标注可以提高动态Web页的标注效率。针对动态Web页以查询生成居多的特点，提出标注与反馈相结合的深度标注框架，即第一步通过标注模块进行初步标注，并记录Web页面的查询要求；第二步分析查询信息，找出不同Web页的关系，通过反馈模块进一步补充标注内容，从而提高标注的质量。     

多源海量遥感数据分页查询策略的优化研究

在Web环境下查询遥感图像数据时,由于数据规模巨大和数据来源众多,常出现页面响应时间过长甚至没有响应的情况。针对传统分页查询方法查询数据量越大,页面响应速度越慢的问题,提出一种基于页面转换算法的多源海量遥感数据分页查询优化策略。调用Web服务获取各个数据源中满足条件的记录总数,利用页面转换算法实现用户查询页面的划分,由智能Agent动态查询与合并各个数据源的子页面。对不同的查询数据量进行实验,结果表明,传统分页查询方法的响应时间随着数据量的增加呈线性增长趋势,而优化后分页查询方法的响应时间基本保持不变。     

用Access97创建Web页面

作为Office 97中文专业版的重要组件之一,Access 97中文版是首次完全本地化的中文桌面数据库管理程序。它的显著特点在于易学、易用、功能强大,且与其他Office应用程序高度集成。Access 97最让人动心的是其对Internet和Intranet的支持。Access 97不仅支持Office 97应用程序共享的超级链接以及Web工具栏,更重要的是它通过内置的“Web发布向导”可以很容易地在Internet和Intranet上发布数据库信息,而且形式灵活多样,既可以是静态的Web页(HTML),也可以是动态生成的Web页(IDC/HTX或者ASP)。在要求不是很高的情况下,Access设置还可以在Intranet中充当数据库服务器的角色。 “Web发布向导”具有以下功能:(1)创建一个静态页面和(或)一个查询Web服务器数据库的动态页面;(2)把Web页面     

基于结果模式的Deep Web数据标注方法

全面准确地标注Deep Web查询结果是Deep Web数据集成的关键问题,但现有的Web数据库标注方法还不能较好地解决该问题,为此提出一种基于结果模式的Deep Web数据标注方法。首先通过结果页面解析和抽取结构化数据来完成数据预处理的工作,并在集成结果模式和待标注数据之间建立正确的语义映射,进而确定Deep Web数据的标注信息。通过对4个领域Web数据库进行实验测试,结果表明所提方法能有效地标注Deep Web查询结果数据。     

深度搜索中下一页链接获取策略的研究

高质量、结构化的数据对很多应用来说其价值是无穷的。然而这些数据往往都隐藏在查询表单背后的深度Web数据库中。深度Web数据库中的内容不能够直接被爬虫通过超链接直接获取,只有通过主动对查询表单提交查询才能有效地获取这些数据。最近的研究主要针对以下两个方面：一是如何有效的理解和获取网络上的查询表单,二是如何不断的对查询表单进行查询并下载表单背后的资源。一个必要的但还没很好的解决的一个问题是：如何将一次查询所获得的所有结果页全部有效的获得？这个问题从本质上看就是如何获取结果页面中＂下一页＂超链接的问题。本文针对这一问题提出了一种通过分析＂下一页＂超链接的结构特征来识别＂下一页＂超链接的策略,并在多种情况下进行了试验,取得了良好的效果。     

基于Ajax 和PHP 数据分页的实现

Web系统经常会遇到大量的数据分页显示问题,传统的分页技术在提取数据时用户等待时间较长,利用Ajax技术结合PHP语言开发的Web系统在实现大量数据分页显示时,不需重载整个页面,通过更新局部数据即可,这样不但减少了用户等待时间,而且提高了查询效率。下面结合实例谈谈数据的动态分页显示的实现问题。     

Unix平台下的数据库查询开发工具(Web-DTools)的设计与实现

一个用于查询的界面函数库,只要写简单的几句话,就马上就可以生成对表数据的Web交互查询动态页面。     

Web页面自动化设计中布局挖掘和样式匹配算法

具有相似功能的Web应用,其页面样式和布局往往存在很大的相似性。针对当前Web页面开发复杂度高且效率低的情况,提出一种挖掘现有页面布局结构和样式属性的方法来实现Web页面自动化设计。该方法充分利用Web网页布局结构上的特点,采用分级处理的方式,首先利用页面分块算法思想通过相似度计算挖掘出具有相似性的代码块,其次通过结合RoSunday方法解析样式文件快速匹配出节点集合对应的样式表并建立文档模型树结构,各个子模块之间的相互组合可以实现页面的自动化设计。通过应用实例表明,该方法能动态地设计并生成页面,有效提升Web页面开发效率。     

一种Deep Web爬虫爬行策略

Deep Web包含丰富的、高质量的信息资源,由于没有直接指向Deep Web页面的静态链接,目前大多搜索引擎不能发现这些页 面,只能通过填写表单提交查询获取。为此,提出一种Deep Web爬虫爬行策略。用网页分类器的分层结果指导链接信息提取器提取有前途的链接,将爬行深度限定在3层,从最靠近查询表单中提取链接,且只提取属于这3个层次的链接,从而减少爬虫爬行时间,提高爬虫的准确度,并设计聚焦爬行算法的约束条件。实验结果表明,该策略可以有效地下载Deep Web页面,提高爬行效率。     

基于链接关系的Web页面相似度搜索

Web页面相似度搜索对于网络新闻推荐、近似查询等研究领域具有重要作用。SimRank是经典的相似度计算模型,但其预计算时间和空间开销非常巨大,不适用大规模Web页面网络。利用SimRank快速收敛的特点,在SimRank基础上提出高效Web页面相似度搜索方法(WSR),预计算1步迭代相似度矩阵,根据预计算的1步迭代相似度矩阵在线计算给定查询页面和其他页面的2步迭代相似度。通过对Web网络进行静态剪枝,进一步提高预计算和在线查询处理的效率。实验结果显示,WSR显著降低了存储开销和预计算时间开销,且具有较高精确度和快速查询响应时间。     

Unveiling the hidden bride: deep annotation for mapping and migrating legacy data to the Semantic Web

The success of the Semantic Web crucially depends on the easy creation, integration, and use of semantic data. For this purpose, we consider an integration scenario that defies core assumptions of current metadata construction methods. We describe a framework of metadata creation where Web pages are generated from a database and the database owner is cooperatively participating in the Semantic Web. This leads us to the deep annotation of the database—directly by annotation of the logical database schema or indirectly by annotation of the Web presentation generated from the database contents. From this annotation, one may execute data mapping and/or migration steps, and thus prepare the data for use in the Semantic Web. We consider deep annotation as particularly valid because: (i) dynamic Web pages generated from databases outnumber static Web pages, (ii) deep annotation may be a very intuitive way to create semantic data from a database, and (iii) data from databases should remain where it can be handled most efficiently—in its databases. Interested users can then query this data directly or choose to materialize the data as RDF files.     

Deep Web crawling: a survey

World Wide Web - Deep Web crawling refers to the problem of traversing the collection of pages in a deep Web site, which are dynamically generated in response to a particular query that is...     

基于本体的Deep Web数据标注

借鉴语义Web领域中深度标注的思想,提出了一种对Web数据库查询结果进行语义标注的方法.为了获得完整且一致的标注结果,将领域本体作为Web数据库遵循的全局模式引入到查询结果语义标注过程中.对查询接口及查询结果特征进行详细分析,并采用查询条件重置的策略,从而确定查询结果数据的语义标记.通过对多个不同领域Web数据库的测试,在具有领域本体支持的条件下,该方法能够对Web数据库查询结果添加正确的语义标记,从而验证了该方法的有效性.     

Beyond Single-Page Web Search Results

Given a user keyword query, current Web search engines return a list of individual Web pages ranked by their "goodness" with respect to the query. Thus, the basic unit for search and retrieval is an individual page, even though information on a topic is often spread across multiple pages. This degrades the quality of search results, especially for long or uncorrelated (multitopic) queries (in which individual keywords rarely occur together in the same document), where a single page is unlikely to satisfy the user's information need. We propose a technique that, given a keyword query, on the fly generates new pages, called composed pages, which contain all query keywords. The composed pages are generated by extracting and stitching together relevant pieces from hyperlinked Web pages and retaining links to the original Web pages. To rank the composed pages, we consider both the hyperlink structure of the original pages and the associations between the keywords within each page. Furthermore, we present and experimentally evaluate heuristic algorithms to efficiently generate the top composed pages. The quality of our method is compared to current approaches by using user surveys. Finally, we also show how our techniques can be used to perform query-specific summarization of Web pages.     

XML技术在化学深层网数据提取中的应用

Internet上的化学数据库是宝贵的化学信息资源,如何有效地利用这些数据是化学深层网所要解决的问题。本文总结了化学深层网的特点,基于XML技术实现从数据库检索返回的半结构化HTML页面中提取数据的目标,使之成为可供程序直接调用做进一步计算的数据。在数据提取过程中,先采用JTidy规范化HTML,得到格式上完整、内容无误的XHTML文档,利用包含着XPath路径语言的XSLT数据转换模板实现数据转换和提取。其中XPath表达式的优劣决定了XSLT数据转换模板能否长久有效地提取化学数据,文中着重介绍了如何编辑健壮的XPath表达式,强调了XPath表达式应利用内容和属性特征实现对源树中数据的定位,并尽可能地降低表达式之间的耦合度,前瞻性地预测化学站点可能出现的变化并在XSLT数据转换模板中采取相应的措施以提高表达式的长期有效性。为创建化学深层网数据提取的XSLT数据提取模板提供方法指导。     

ViDE: A Vision-Based Approach for Deep Web Data Extraction

Deep Web contents are accessed by queries submitted to Web databases and the returned data records are enwrapped in dynamically generated Web pages (they will be called deep Web pages in this paper). Extracting structured data from deep Web pages is a challenging problem due to the underlying intricate structures of such pages. Until now, a large number of techniques have been proposed to address this problem, but all of them have inherent limitations because they are Web-page-programming-language-dependent. As the popular two-dimensional media, the contents on Web pages are always displayed regularly for users to browse. This motivates us to seek a different way for deep Web data extraction to overcome the limitations of previous works by utilizing some interesting common visual features on the deep Web pages. In this paper, a novel vision-based approach that is Web-page-programming-language-independent is proposed. This approach primarily utilizes the visual features on the deep Web pages to implement deep Web data extraction, including data record extraction and data item extraction. We also propose a new evaluation measure revision to capture the amount of human effort needed to produce perfect extraction. Our experiments on a large set of Web databases show that the proposed vision-based approach is highly effective for deep Web data extraction.     

Databases deepen the Web

The Web has become the preferred medium for many database applications, such as e-commerce and digital libraries. These applications store information in huge databases that users access, query, and update through the Web. Database-driven Web sites have their own interfaces and access forms for creating HTML pages on the fly. Web database technologies define the way that these forms can connect to and retrieve data from database servers. The number of database-driven Web sites is increasing exponentially, and each site is creating pages dynamically-pages that are hard for traditional search engines to reach. Such search engines crawl and index static HTML pages; they do not send queries to Web databases. The information hidden inside Web databases is called the "deep Web" in contrast to the "surface Web" that traditional search engines access easily. We expect deep Web search engines and technologies to improve rapidly and to dramatically affect how the Web is used by providing easy access to many more information resources.     

Web Data Extraction from Query Result Pages Based on Visual and Content Features

A rapidly increasing number of Web databases are now become accessible via their HTML form-based query interfaces. Query result pages are dynamically generated in response to user queries, which encode structured data and are displayed for human use. Query result pages usually contain other types of information in addition to query results, e.g., advertisements, navigation bar etc. The problem of extracting structured data from query result pages is critical for web data integration applications, such as comparison shopping, meta-search engines etc, and has been intensively studied. A number of approaches have been proposed. As the structures of Web pages become more and more complex, the existing approaches start to fail, and most of them do not remove irrelevant contents which may affect the accuracy of data record extraction. We propose an automated approach for Web data extraction. First, it makes use of visual features and query terms to identify data sections and extracts data records in these sections. We also represent several content and visual features of visual blocks in a data section, and use them to filter out noisy blocks. Second, it measures similarity between data items in different data records based on their visual and content features, and aligns them into different groups so that the data in the same group have the same semantics. The results of our experiments with a large set of Web query result pages in di?erent domains show that our proposed approaches are highly effective.     

通用查询模块的设计与实现

提出了一个用于任意Web信息系统的通用查询模块,它通过在配置文件中定义查询的方法取代了繁琐的查询页面的编写,使查询功 能开发迅速、维护简便。文中介绍了模块的设计思想、工作方式、组成结构。详细讨论了各部分的实现技术与核心算法,并通过性能分析论 证了模块的巨大优势。     

Deep Web数据集成研究综述

随着World Wide Web(WWW)的飞速发展,Deep Web中蕴含了海量的可供访问的信息,并且还在迅速地增长.这些信息要通过查询接口在线访问其后端的Web数据库.尽管丰富的信息蕴藏在Deep Web中,由于Deep Web数据的异构性和动态性,有效地把这些信息加以利用是一件十分挑战性的工作.Deep Web数据集成至今仍然是一个新兴的研究领域,其中包含有若干需要解决的问题.总体来看,在该领域已经开展了大量的研究工作,但各个方面发展并不均衡.文中提出了一个Deep Web数据集成的系统架构,依据这个系统架构对Deep Web数据集成领域中若干关键研究问题的现状进行了回顾总结,并对未来的研究发展方向作了较为深入的探讨分析.