SharePoint Portal Server可视元件——使用Web部件和门户列表帮助用户导航门户

SharePoint的另一个重要部分是可视元件，包括Web部件和视图。本文将介绍SharePoint的信息表达功能。特别是列表视图和分组列表Web部件。以及如何使用它们来集合信息。从而为用户提供更全面和直观的界面。[编者按]     

基于网页格局的内容分块算法

随着移动上网业务的日益发展，人们迫切希望能够通过手持终端设备访问丰富的Web内容。同时，由于手持终端设备存在着多方面的局限性，使得必须对所要访问的Web页面进行转换处理。本文提出了一种新的内容分块算法，能够智能化地通过分析内容关系对Web页面信息进行分块和抽取，使得手持终端设备用户能够快速、高效地访问Web内容。     

应用词法分析方法抽取Web查询接口模式

Deep Web中蕴含着大量高质量的数据,然而只有通过Web查询接口对Web数据库提交查询才能获取这些数据,因此,自动获取Web查询接口模式是实现Web数据库集成的关键.将Web查询接口模式的抽取过程看作一个词法分析的过程,通过构建EGLM-FA(元素分组及标签匹配有限状态自动机)来完成对Web查询接口模式的抽取.首先应用Html呈现引擎将Web查询接口所在页面进行解析,利用查询接口Form中的DOM节点及其坐标信息构建相应的NSS(节点空间结构),之后再将所有的NSS组成NSS列表,将NSS列表作为EGLM-FA的输入,进而抽取出Web查询接口的模式.     

基于种子自扩展的命名实体关系抽取方法

命名实体间关系的抽取是信息抽取中的一个重要研究问题，该文提出了一种从大量的文本集合中自动抽取命名实体间关系的方法，找出了所有出现在同一句子内、词语之间的距离在一定范围之内的命名实体对，把它们的上下文转化成向量。手工选取少量具有抽取关系的命名实体对，把它们作为初始关系的种子集合，通过自学习，关系种子集合不断扩展。通过计算命名实体对和关系种子之间的上下文相似度来得到所要抽取的命名实体对。通过扩展关系种子集合的方法，抽取的召回率和准确率都得到了提高。该方法在对《人民日报》语料库的测试中，取得了加权平均值F-Score为0.813的效果。     

如何提取门户网站新闻主题

随着网络新闻类型门户网站的不断增多,如何从纷繁复杂的Web信息中得到我们需要的新闻标题成为了一个问题。这里通过正则表达式对门户网站新闻主页进行主题抽取,得到新闻主题列表,为用户访问网络提供方便。     

基于本体关系匹配的信息抽取

目前，稳定可靠的信息抽取是一个有待解决的问题，该文提出了基于本体关系匹配信息抽取算法，应用语义Web把信息抽取目标文档描述为特殊的本体格式，采用机器学习理论对本体进行分析和处理。测试数据集的实验结果显示，本体关系集匹配算法优于其他4种信息抽取算法。     

表格信息抽取引擎的设计与实现

讨论针对Web表格的信息抽取，分析并给出了表格信息抽取引擎的系统结构，以及实现该系统所涉及的关键技术和数据模型，为用户提供一种以Web表格为信息抽取对象的、支持抽取方式选择的Web表格信息抽取工具。     

基于扩展标记图的Web信息抽取器

介绍了一种新的Web信息抽取器，该抽取器基于扩展标记图模型，实观了数据和模式的分离，应用于Web检索系统中，能够有效地支持标记级实时信息检索、抽取和重组。还介绍了其在Web信息检索系统PowerSearcher中的实际应用。     

Web信息的自主抽取方法

提出了基于表格结构及列表结构的W eb页面信息自主抽取的方法。可根据用户对信息的需求自主地从相关页面中抽取信息并将抽取信息按关系模型进行重组存放在数据库中,对表格结构信息源仅需标注一页网页,即可获取抽取知识,通过自学习能够较好地适应网页信息的动态变化,实现信息的自动抽取。对列表结构信息源信息,通过对DOM树结构的分析,动态获得信息块在DOM层次结构中的路径,根据信息对象基本的抽取知识,获得信息对象值。采用自学习的方法以适应网页信息的动态变化。     

Web信息抽取的研究

本文分析了Web信息抽取的概念、特点，总结了Web信息抽取技术的分类、技术发展现状及其应用。描述了Web信息抽取的知识来源，并对Web信息抽取的几类典型方法进行了详细描述。     

Web table discrimination with composition of rich structural and content information

A table is a well-organized and summarized knowledge expression for a domain. Therefore, it is of great importance to extract information from tables. However, many tables in Web pages are used not to transfer information but to decorate pages. One of the most critical tasks in Web table mining is thus to discriminate meaningful tables from decorative ones. The main obstacle of this task comes from the difficulty of generating relevant features for discrimination. This paper proposes a novel discrimination method using a composite kernel which combines parse tree kernels and a linear kernel. Because a Web table is represented as a parse tree by an HTML parser, it is natural to represent the structural information of a table as a parse tree. In this paper, two types of parse trees are used to represent structural information within and around a table. These two trees define the structure kernel that handles the structural information of tables. The contents of a Web table are manipulated by a linear kernel with content features. Support vector machines with the composite kernel distinguish meaningful tables from decorative ones with high accuracy. A series of experiments show that the proposed method achieves state-of-the-art performance.     

Transforming arbitrary tables into logical form with TARTAR

The tremendous success of the World Wide Web is countervailed by efforts needed to search and find relevant information. For tabular structures embedded in HTML documents, typical keyword or link-analysis based search fails. The Semantic Web relies on annotating resources such as documents by means of ontologies and aims to overcome the bottleneck of finding relevant information. Turning the current Web into a Semantic Web requires automatic approaches for annotation since manual approaches will not scale in general. Most efforts have been devoted to automatic generation of ontologies from text, but with quite limited success. However, tabular structures require additional efforts, mainly because understanding of table contents requires the comprehension of the logical structure of the table on the one hand, as well as its semantic interpretation on the other. The focus of this paper is on the automatic transformation and generation of semantic (F-Logic) frames from table-like structures. The presented work consists of a methodology, an accompanying implementation (called TARTAR) and a thorough evaluation. It is based on a grounded cognitive table model which is stepwise instantiated by the methodology. A typical application scenario is the automatic population of ontologies to enable query answering over arbitrary tables (e.g. HTML tables).     

Web表格信息抽取模型的设计与实现

Web表格作为一种简洁有效的数据信息表达方式,已广泛应用于Web页面中.现提出一种基于表格结构的Web表格信息抽取模型,该模型主要有表格定位模块、表格结构预处理模块和表格信息抽取与重构模块三个模块组成,根据Web表格的结构标记和自定义的启发式规则来抽取表格信息.实验结果表明该模型能够很好地应用于Web表格信息的抽取.     

Harvesting relational tables from lists on the web

A large number of web pages contain data structured in the form of ??lists??. Many such lists can be further split into multi-column tables, which can then be used in more semantically meaningful tasks. However, harvesting relational tables from such lists can be a challenging task. The lists are manually generated and hence need not have well-defined templates??they have inconsistent delimiters (if any) and often have missing information. We propose a novel technique for extracting tables from lists. The technique is domain independent and operates in a fully unsupervised manner. We first use multiple sources of information to split individual lines into multiple fields and then, compare the splits across multiple lines to identify and fix incorrect splits and bad alignments. In particular, we exploit a corpus of HTML tables, also extracted from the web, to identify likely fields and good alignments. For each extracted table, we compute an extraction score that reflects our confidence in the table??s quality. We conducted an extensive experimental study using both real web lists and lists derived from tables on the web. The experiments demonstrate the ability of our technique to extract tables with high accuracy. In addition, we applied our technique on a large sample of about 100,000 lists crawled from the web. The analysis of the extracted tables has led us to believe that there are likely to be tens of millions of useful and query-able relational tables extractable from lists on the web.     

Creating the ultimate research assistant

Looks at the custom tool developed by the author that leverages the Google Web search API (or a similar search service) to discover a list of Web pages matching a given topic; identify and extract trends and patterns from these Web pages' text; and transform those trends and patterns into an understandable, useful, and well-organized information resource. The tool accomplishes these tasks using four main components. First, a search engine client discovers a list of relevant Web pages using the Google Web search API. An information extraction engine then mines concepts and associated text passages from these Web pages. Next, a clustering engine organizes the most significant concepts into a hierarchical taxonomy. Finally, a knowledge base generator uses this taxonomy to generate a hypertext knowledge base from the extracted concepts and text passages.     

Visualizing Web search results in 3D

The exponential growth in Web sites is making it increasingly difficult to extract useful information on the Internet using existing search engines. Despite a wide range of sophisticated indexing and data retrieval features, search engines often deliver satisfactory results only when users know precisely what they are looking for. Traditional textual interfaces present results as a list of links to Web pages. Because most users are unwilling to explore an extensive list, search engines arbitrarily reduce the number of links returned, aiming also to provide quick response times. Moreover, their proprietary ranking algorithms often do not reflect individual user preferences. Those who need comprehensive general information about a topic or have vague initial requirements instead want a holistic presentation of data related to their queries. To address this need, we have developed Periscope, a 3D search result visualization system that displays all the Web pages found in a synthetic, yet comprehensible format.     

SQL Test Suite goes online

Does your relational database speak SQL fluently? It's easy to find out, because the SQL (Structured Query Language) Test Suite is now free on the Web. SQL is the standard that lets DBMS products from different vendors interoperate. It defines common data structures (tables, columns, views, and so on) and provides a data manipulation language to populate, update, and query those structures. Accessing structured data with SQL is quite different from searching the full text of documents on the Web. Structured data in the relational model means data that can be represented in tables. Each row represents a different item, and the columns represent various attributes of the item. Columns have names and integrity constraints that specify valid values. Because the column values are named and represented in a consistent format, you can select rows precisely, on the basis of their contents. This is especially helpful in dealing with numeric data. You can also join data from different tables on the basis of matching column values. It is possible to do useful types of analysis too, listing items that are in one table and are missing, present, or have specific attributes in a related table. You can extract from a large table precisely those rows of interest, regroup them, and generate simple statistics on them     

一种基于未知结构网页抽取本体的方法

在Web上数据大多是结构化的,但事先并不熟知数据的结构,因此不能有效地查询感兴趣的数据.提出了一种独立于文本抽取本体的方法,其过程包括表的理解、数据集成和本体生成,其中表理解是搜寻定位兴趣表、识别及匹配属性和值,并形成记录;数据集成是匹配源记录和目标模式;本体卷积是将源记录的数据抽取到目标模式.结果表明这种方法可以通过已知的目标模式有效地抽取未知结构的数据.     

扩展UDDI以支持语义信息的研究

介绍了一种扩展UDDI以支持语义信息的方法,即在注册Web服务时添加语义信息,并支持基于语义的查询。首先在UDDI系统中加入一个领域本体库,再为该UDDI中的每个注册服务添加语义信息,并将服务和本体库的对应关系存入到UDDI的数据库中。在服务申请者查询Web服务时,由用户提供语义查询模板,根据用户描述的本体语义信息得到候选服务列表,再根据用户对服务质量的要求计算候选服务的匹配度,将候选服务依照其匹配度的大小顺序返回给用户。