EAGER: Exploring and Linking Widely Distributed Data on the Semantic Web

EAGER：探索和链接语义网上广泛分布的数据

• 批准号: 1143585
• 负责人: James Hendler
• 金额: $ 19.74万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1143585&HistoricalAwards=false
• 关键词: EAGER; Exploring; Linking; Widely; Distributed

The goal of this project is to explore key algorithms, technologies and protocols that will lead to the next level of development of the original Semantic Web vision of the 'Web of Data', a Web in which the unstructured texts of the current Web are integrated in a seamless way with information currently locked in structured databases. While a huge amount of open data is being made available on the Web, especially in the 'Open Government Data' arena, traditional computing techniques are inadequate for finding this data, for linking it to other data, and for reusing and repurposing the data resources. The project aims to show that an innovative combination of Semantic Web technologies will provide the basis for a new approach to large-scale, on-line, data integration and use.The research team will demonstrate our techniques by showing their efficacy on a combination of Open Government datasets being released around the world. There are already hundreds of thousands of these databases made available in machine-readable formats by countries, municipalities and cities, and the number is growing exponentially. This makes Open Government Data a large-scale testbed for Web-based data integration. The research team has collected the metadata for close to 400,000 datasets from more than 60 catalogs, from 20 countries, which are published in fourteen different languages. The project will show how the combination of linked-data representations, machine-readable metadata and Semantic Web ontologies will provide an ability to federate data across these catalogs, domains, and cultures. The researchers will develop the foundational algorithms that make it possible for researchers to find, access, integrate and analyze ad hoc combinations of these many datasets integrated on the fly. Thus, the outcome of this project will be to demonstrate techniques, and develop a proof-of-concept demonstration, showing that the integration of multiple data sources across the Web can be accomplished by the application of a combination of semantic information of different kinds. The researchers will show that it is possible to build search and reuse tools that function across large distributed data collections, and we will explore the key research challenges in creating Web-scale linked-open-data repositories. The success of this project will demonstrate that by bridging the gap between structured and unstructured sources, it is possible to develop techniques that set the stage for a second generation of more powerful Semantic Web tools. Such tools will allow scientists, engineers, and eventually end users to perform a range of analyses without needing the large proprietary data resources currently available to only a small set of researchers working in companies with access to 'big data'. Additional information about the project can be found at: http://data.rpi.edu

该项目的目标是探索关键算法、技术和协议，这些算法、技术和协议将引领“数据网”原始语义网愿景的下一阶段发展，在这种网络中，当前网络的非结构化文本与当前锁定在结构化数据库中的信息以一种无缝的方式集成在一起。当大量的开放数据，特别是在“开放政府数据”领域，在网络上提供时，传统的计算技术不足以找到这些数据，将其与其他数据连接起来，以及重新使用和重新利用数据资源。该项目旨在展示语义网技术的创新组合将为大规模、在线、数据集成和使用的新方法提供基础。研究小组将通过展示我们的技术在世界各地发布的开放政府数据集组合上的功效来展示我们的技术。各国、各直辖市和城市已经以机器可读的格式提供了数十万个这样的数据库，而且这个数字还在呈指数级增长。这使得开放政府数据成为基于web的数据集成的大规模测试平台。该研究小组已经从20个国家的60多个目录中收集了近40万个数据集的元数据，这些数据集以14种不同的语言出版。该项目将展示链接数据表示、机器可读元数据和语义Web本体的组合将如何提供跨这些目录、域和区域性联合数据的能力。研究人员将开发基础算法，使研究人员能够查找、访问、集成和分析这些即时集成的许多数据集的特别组合。因此，该项目的结果将是演示技术，并开发概念验证演示，显示跨Web的多个数据源的集成可以通过不同类型的语义信息的组合应用程序来完成。研究人员将展示在大型分布式数据集合中构建搜索和重用工具是可能的，我们将探索在创建网络规模的链接开放数据存储库方面的关键研究挑战。这个项目的成功将证明，通过弥合结构化和非结构化源之间的差距，可以开发出为第二代更强大的语义Web工具奠定基础的技术。这些工具将允许科学家、工程师和最终用户执行一系列分析，而不需要大量的专有数据资源，目前只有一小部分在公司工作的研究人员可以访问“大数据”。有关该项目的更多信息可以在http://data.rpi.edu上找到