SBIR Phase I: Unsupervised Extraction of Relational Data from the Web

SBIR 第一阶段：无监督地从网络中提取关系数据

• 批准号: 0441563
• 负责人: Steven Minton
• 金额: $ 10万
• 依托单位: FETCH TECHNOLOGIES
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-01-01 至 2005-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0441563&HistoricalAwards=false
• 关键词: SBIR; Phase; Unsupervised; Extraction; Relational

This Small Business Innovation Research (SBIR) Phase I research project will enable software systems to make use of data on the Web. The semantic web is intended to allow data to be shared and used by software applications. Unfortunately, in the present world, data on the Web is generally inaccessible to most applications because it is presented in a format intended to be usable by humans, as opposed to computers. The ultimate goal is to create a relational view of data on the web, so that applications can access Web data based on entities and their relations. This project proposes to achieve this with an unsupervised machine learning approach that extracts data from web sites and converts it into relational form. It will develop and implement an unsupervised algorithm that takes advantage of multiple heterogeneous types patterns found on web sites, including the link structure, formatting conventions, and content regularities. This project will result in a powerful new generation of Web harvesting technology that has clear commercial value. In addition moreover, it will enable the vision of the semantic web to become a reality. Web harvesting is an area of growing commercial interest for a variety of vertical markets, including Sales Intelligence, Market Intelligence, News Aggregation, and Background Search. However, web-harvesting technology is limited today, since the collection of rich, detailed data must be done on a site-by-site basis. The approach described here, if successful, will enable a new generation of intelligent Web harvesting technology that can scale to the entire Web. Ultimately, our approach will enable applications to query the entire Web as if it were a relational database. This has tremendous commercial value, and moreover, will enable many new types of web applications to be developed. In addition to the commercial value, the technical approach is novel and has significant merits on its own. If it is successful, the proposed method should generalize to other complex domains (such as scene understanding and natural language processing) where multiple heterogeneous types of structure must be analyzed to discover underlying meaning

小企业创新研究（SBIR）第一阶段研究项目将使软件系统能够利用网络上的数据。语义网旨在允许软件应用程序共享和使用数据。不幸的是，在当今世界中，Web上的数据通常无法被大多数应用程序访问，因为它是以旨在供人类使用的格式呈现的，而不是计算机。最终目标是在Web上创建数据的关系视图，以便应用程序可以基于实体及其关系访问Web数据。该项目提出了一种无监督的机器学习方法来实现这一目标，该方法从网站中提取数据并将其转换为关系形式。它将开发和实现一种无监督算法，该算法利用网站上发现的多种异构类型模式，包括链接结构，格式约定和内容转换。这个项目将产生一个强大的新一代网络收集技术，具有明显的商业价值。此外，它将使语义网的愿景成为现实。Web收获是一个越来越受各种垂直市场关注的商业领域，包括销售情报，市场情报，新闻聚合和背景搜索。然而，今天的网络收集技术是有限的，因为收集丰富，详细的数据必须在一个网站一个网站的基础上完成。这里描述的方法，如果成功的话，将使新一代的智能网络收集技术，可以扩展到整个网络。最终，我们的方法将使应用程序能够查询整个Web，就好像它是一个关系数据库。这具有巨大的商业价值，而且将使许多新类型的Web应用程序得以开发。除了商业价值之外，该技术方法是新颖的，并且具有其自身的显著优点。如果它是成功的，所提出的方法应该推广到其他复杂的领域（如场景理解和自然语言处理），其中必须分析多种异构类型的结构以发现潜在的含义