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

小企业创新研究（SBIR）第二阶段项目将使软件系统能够利用嵌入HTML页面的Web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。 这个项目的目标是在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。 该方法使用无监督机器学习从网站中提取数据，以转换为关系形式。该项目将产生新一代具有明显商业价值的网络采集技术。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，并将使许多新类型的Web应用程序得以开发。除了商业价值外，技术方法新颖，本身就有很大的优点。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），其中必须分析多个异构类型的结构，以发现潜在的意义。

This Small Business Innovation Research (SBIR) Phase II project will enable software systems to make use of data on the Web that is embedded in HTML pages. 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 goal of this project is to create a relational view of data on the Web, so that applications can access Web data based on entities and their relations. The approach uses unsupervised machine learning to extract data from web sites for conversion into relational form. This project will result in a new generation of Web harvesting technology that has clear commercial value. 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 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.