Deep Web查询接口的自动判定

传统搜索引擎仅可以索引浅层Web页面．然而在网络深处隐含着大量、高质量的信息，传统搜索引擎由于技术原因不能索引这些被称之为Deep Web的页面。由于查询接口是Deep Web的唯一入口，因此要获取Deep Web信息就需判定哪些网页表单是Deep Web查询接口。文中介绍了一种利用朴素贝叶斯分类算法自动判定网页表单是否为Deep Web查询接口的方法，并实验验证了该方法的有效性。     

Deep Web查询接口的判定技术研究

互联网的飞速发展,给人类带来了海量的可供访问信息,但是,现今搜索引擎索引的绝大部分是表层Surface Web网的信息,限于一些技术原因,搜索引擎几乎无法索引到Deep Web网中的信息。由于查询接口是Deep Web的唯一入口,但并非所有的网页表单都是查询接口,为了能充分利用Deep Web后台数据库信息,首先要找到进入Deep Web后台数据库的入口,所以对查询接口的正确判定至关重要。文中介绍了利用决策树CA．5分类算法自动判定网页表单是否为Deep Web查询接口的方法。     

基于返回结果的DeepWeb查询接口识别

互联网上存在许多有价值的信息,搜索引擎只能索引静态页面,无法索引DeepWeb数据,而Deep Web通常以表单形式存在,只有提交表单查询才能获得其数据,如何发现和识别Deep web查询接口成为人们关注的问题.在分析表单表现形式与功能内在的联系的基础上,提出一个表单的抽象模型,依此过滤非Deep Web查询接口的表单.通过对返回结果页面分析方法,实现Deep W出查询接口的识别,实验结果证明了该方法的有效性.     

基于最大熵分类器的Deep Web查询接口自动判定

Web中包含着海量的高质量信息,它们通常处在网络深处,无法被传统搜索引擎索引,将这样的资源称为Deep Web。因为查询接口是Deep Web的唯一入口,所以要获取Deep Web信息就必须判定哪些网页表单是Deep Web查询接口。由于最大熵模型可以综合观察到的各种相关或不相关的概率知识,对许多问题的处理都可以达到较好的结果。因此,基于最大熵模型的分类性能,利用最大熵分类算法自动判定查询接口。并通过实验,将最大熵分类法与其它常用分类方法进行了比较,结果显示它的分类性能优于Bayes方法和C4.5方法,与SVM方法相当,表明这是一种非常实用的查询接口分类方法。     

一种Deep Web爬虫的设计与实现

随着World Wide Web的快速发展,Deep Web中蕴含了越来越多的可供访问的信息.这些信息可以通过网页上的表单来获取,它们是由Deep Web后台数据库动态产生的.传统的Web爬虫仅能通过跟踪超链接检索普通的Surface Web页面,由于没有直接指向Deep Web页面的静态链接,所以当前大多数搜索引擎不能发现和索引这些页面.然而,与Surface Web相比,Deep Web中所包含的信息的质量更高,对我们更有价值.本文提出了一种利用HtmlUnit框架设计Deep Web爬虫的方法.它能够集成多个领域站点,通过分析查询表单从后台数据库中检索相关信息.实验结果表明此方法是有效的.     

Deep Web爬虫爬行策略研究

如今Web上越来越多的信息可以通过查询接口来获得，为了获取某Deep Web站点的页面用户不得不键入一系列的关键词集。由于没有直接指向Deep Web页面的静态链接，当前大多搜索引擎不能发现和索引这些页面。然而，近来研究表明Deep Web站点提供的高质量的信息对许多用户来说是非常有价值。这里研究了怎样建立起一个有效的Deep Web爬虫，它可以自动发现和下载Deep Web页面。由于Deep Web惟一“入口点”是查询接口，Deep Web爬虫设计面对的主要挑战是怎样对查询接口自动产生有意义的查询。这里提出一种针对查询接口查询自动产生问题的理论框架。通过在实际Deep Web站点上的实验证明了此方法是非常有效的。     

Deep Web接口的自动识别技术研究

获取Deep Web中信息的主要途径是通过在其提供的查询接口上提交查询来实现的,目前大部分的研究以表单内的

标签获得表单内容结构,判断是不是一个Deep Web查询接口。提出了接口块的概念,设计了一种基于页面信息和视觉信息的接口块定位方法,最后将判定接口块是不是Deep Web接口看作是一个模式识别的分类问题,通过抽取适当的表单结构特征,采用C4.5决策树和SVM相结合的分类算法来进行接口块的判定,得到页面中含有的Deep Web查询接口。采用UIUC的TEL-8数据集进行实验,结果表明,该方法的准确率达到了97.30%,具有良好的可行性和实用性。     

基于决策树和链接相似的Deep Web查询接口判定*

针对现有Deep Web查询接口判定方法误判较多、无法有效区分搜索引擎类接口的不足,提出了基于决策树和链接相似的Deep Web查询接口判定方法。该方法利用信息增益率选取重要属性,并构建决策树对接口表单进行预判定,识别特征较为明显的接口;然后利用基于链接相似的判定方法对未识别出的接口进行二次判定,准确识别真正查询接口,排除搜索引擎类接口。结果表明,该方法能有效区分搜索引擎类接口,提高了分类的准确率和查全率。     

Deep Web查询接口研究

Deep Web查询接口是Web数据库的接口,其对于Deep Web数据库集成至关重要。本文根据网页表单的结构特征定义查询接口;针对非提交查询法,给出界定Deep Web查询接口的一些规则;提出提交查询法,根据链接属性的特点进行判断,找到包含查询接口的页面;采用决策树C4.5算法进行分类,并用Java语言实现Deep Web查询接口系统。     

DeepWeb信息集成系统中查询转换

随着Internet信息的迅速增长,许多Web信息已经被各种各样的可搜索在线数据库所深化,并被隐藏在Web查询接口下面.传统的搜索引擎由于技术原因不能索引这些信息--Deep Web信息.由于Deep Web惟一"入口点"是查询接口.为使查询接口自动产生有意义有查询,给出了Deep Web信息集成系统框架,提出了基于数据类型的搜索驱动的用户查询转换方法,基于此设计并实现了一个针对中文Deep Web信息集成原型系统.通过在实际Deep web站点上的实验证明了此方法是非常有效的.     

WebDB: A System for Querying Semi-structured Data on the Web

The World-Wide Web can be viewed as a collection of semi-structured multimedia documents in the form of Web pages connected through hyperlinks. Unlike most web search engines, which primarily focus on information retrieval functionality, WebDB aims at supporting a comprehensive database-like query functionality, including selection, aggregation, sorting, summary, grouping, and projection. WebDB allows users to access (1) document level information, such as title, URL, length, keywords types and last modified date; (2) intra-document structures, such as tables, forms and images and (3) inter-document linkage information, such as destination URLs and anchors. With these three types of information, comprehensive queries for complex Web-based applications, such as Web mining and Web site management, can be answered. WebDB is based on object-relational concepts: Object-oriented modeling and relational query language. In this paper, we present the data model, language and implementation of WebDB. We also present the novel visual query/browsing interface for semi-structured Web and Web documents. Our system provides high usability compared with other existing systems.     

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.     

基于标记树对象抽取技术的Hidden Web获取研究

目前标准的搜索引擎能够检索的仅仅是WorldWideWeb提供的小部分称为可索引的Web信息。大量的HiddenWeb信息(估计容量是可索引Web的500倍)对这些搜索引擎是不可见的。这些信息隐藏在Web页面的搜索表单后面,保存在大型的动态数据库中。该文提出了一套检索HiddenWeb信息的方法,给出了系统的框架结构,并详细讨论了实现的关键技术。系统采用新的基于标记树的对象抽取(Tag-Tree-basedObjectExtraction)方法自动地从Web页面中抽取HiddenWeb信息,然后在此基础上给出了结构化的HiddenWeb信息查询算法。文章最后对实验结果进行了讨论。     

DeepWeb信息集成系统中查询转换

随着Internet信息的迅速增长,许多Web信息已经被各种各样的可搜索在线数据库所深化,并被隐藏在Web查询接口下面。传统的搜索引擎由于技术原因不能索引这些信息——DeepWeb信息。由于DeepWeb惟一“入口点”是查询接口,为使查询接口自动产生有意义有查询,给出了DeepWeb信息集成系统框架,提出了基于数据类型的搜索驱动的用户查询转换方法,基于此设计并实现了一个针对中文DeepWeb信息集成原型系统。通过在实际DeepWeb站点上的实验证明了此方法是非常有效的。     

Exploiting location information for Web search

Most Web pages contain location information, which are usually neglected by traditional search engines. Queries combining location and textual terms are called as spatial textual Web queries. Based on the fact that traditional search engines pay little attention in the location information in Web pages, in this paper we study a framework to utilize location information for Web search. The proposed framework consists of an offline stage to extract focused locations for crawled Web pages, as well as an online ranking stage to perform location-aware ranking for search results. The focused locations of a Web page refer to the most appropriate locations associated with the Web page. In the offline stage, we extract the focused locations and keywords from Web pages and map each keyword with specific focused locations, which forms a set of <keyword, location> pairs. In the second online query processing stage, we extract keywords from the query, and computer the ranking scores based on location relevance and the location-constrained scores for each querying keyword. The experiments on various real datasets crawled from nj.gov, BBC and New York Time show that the performance of our algorithm on focused location extraction is superior to previous methods and the proposed ranking algorithm has the best performance w.r.t different spatial textual queries.     

基于GoogleEarth技术的三维GIS研究

该文以Asp.net 2.0为基础,利用Web技术和GoogleEarth平台及其扩展接口(Google地球插件及其API和COM API)开发设计一个虚拟校园管理系统,并以web的形式分别实现了校园地物的基础信息查询与显示以及校园地物的三维展示与分析功能。     

一种基于WatiJ的Deep Web蜘蛛研究

Deep Web中相当一部分内容因为动态网页存在而不能进行有效抓取。为此,设计并实现一种基于Web自动化测试工具——WatiJ的Deep Web网络蜘蛛。阐述利用WatiJ实现用户提交查询表单、循环点击翻页按钮等拟人交互方式的原理,通过实例给出动态网页抓取的关键步骤。实验结果表明,该蜘蛛是针对授权数据源进行动态网页抓取的一种有效解决方案。     

一种基于图模型的Web数据库采样方法

Web数据库中,海量的信息隐藏在具有特定查询能力的查询接口后面,使人无法了解一个Web数据库内容的特征,比如主题的分布、更新的频率等,这就为DeepWeb数据集成带来了巨大的挑战.为了解决这个问题,提出了一种基于图模型的Web数据库采样方法,可以通过查询接口从Web数据库中以增量的方式获取近似随机的样本,即每次查询获取一定数量的样本记录,并且利用已经保存在本地的样本记录生成下一次的查询.该方法的一个重要特点是不受查询接口中属性表现形式的局限,因此是一种一般的Web数据库采样方法.在本地的模拟实验和真实Web数据库上的大量实验表明,该方法可以在较小代价下获得高质量的样本.     

Automatic discovery of Web Query Interfaces using machine learning techniques

The amount of information contained in databases available on the Web has grown explosively in the last years. This information, known as the Deep Web, is heterogeneous and dynamically generated by querying these back-end (relational) databases through Web Query Interfaces (WQIs) that are a special type of HTML forms. The problem of accessing to the information of Deep Web is a great challenge because the information existing usually is not indexed by general-purpose search engines. Therefore, it is necessary to create efficient mechanisms to access, extract and integrate information contained in the Deep Web. Since WQIs are the only means to access to the Deep Web, the automatic identification of WQIs plays an important role. It facilitates traditional search engines to increase the coverage and the access to interesting information not available on the indexable Web. The accurate identification of Deep Web data sources are key issues in the information retrieval process. In this paper we propose a new strategy for automatic discovery of WQIs. This novel proposal makes an adequate selection of HTML elements extracted from HTML forms, which are used in a set of heuristic rules that help to identify WQIs. The proposed strategy uses machine learning algorithms for classification of searchable (WQIs) and non-searchable (non-WQI) HTML forms using a prototypes selection algorithm that allows to remove irrelevant or redundant data in the training set. The internal content of Web Query Interfaces was analyzed with the objective of identifying only those HTML elements that are frequently appearing provide relevant information for the WQIs identification. For testing, we use three groups of datasets, two available at the UIUC repository and a new dataset that we created using a generic crawler supported by human experts that includes advanced and simple query interfaces. The experimental results show that the proposed strategy outperforms others previously reported works.