EAGER: T2K: From Tables to Knowledge

EAGER：T2K：从表格到知识

• 批准号: 1250627
• 负责人: Anupam Joshi
• 金额: $ 20万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1250627&HistoricalAwards=false
• 关键词: EAGER; T2K; Tables; Knowledge

The Web has made humans smarter, providing ready access to vast amounts of knowledge and facts. The Semantic Web has the capacity to similarly enhance computer programs and devices by giving them access to enormous volumes of data, facts and knowledge. This project is exploring the feasibility of automatically extracting new knowledge directly from data found in spreadsheets, database relations, and document tables and representing it as highly interoperable linked open data (LOD) in the Semantic Web language RDF. The extraction is guided by probabilistic graphical models that use statistical information mined from current LOD knowledge resources. To demonstrate the potential payoff of the research, the system is used to extract knowledge from tables collected from medical journals and tables from web sites like data.gov. While the W3C semantic web languages RDF and OWL are used to represent the knowledge, the results are applicable to other semantic data frameworks such as Microdata (Search Consortium), Freebase (Google), Probase (Microsoft) and the Open Graph (Facebook). The open sourced prototype software allows other researchers to experiment with automatically producing semantically enriched data from tables for their domains.If successful, such software extraction systems are expected to become part of a new online knowledge ecology -- both consuming existing LOD knowledge to understand the intended meaning implicit in a table and producing new facts and knowledge that will become part of Web. This represents a dramatic increase in the breadth and depth of public semantic data that can make "big data" analytics more effective.

网络让人类变得更加聪明，可以方便地获取大量知识和事实。语义网有能力通过让计算机程序和设备访问大量数据、事实和知识来增强计算机程序和设备。 该项目正在探索直接从电子表格、数据库关系和文档表中的数据自动提取新知识并将其表示为语义 Web 语言 RDF 中高度可互操作的链接开放数据 (LOD) 的可行性。 提取由概率图形模型指导，该模型使用从当前 LOD 知识资源中挖掘的统计信息。 为了证明研究的潜在回报，该系统用于从医学期刊收集的表格和 data.gov 等网站的表格中提取知识。虽然使用 W3C 语义网络语言 RDF 和 OWL 来表示知识，但结果也适用于其他语义数据框架，例如 Microdata（搜索联盟）、Freebase（Google）、Probase（Microsoft）和 Open Graph（Facebook）。开源原型软件允许其他研究人员尝试从各自领域的表格中自动生成语义丰富的数据。如果成功，此类软件提取系统预计将成为新的在线知识生态的一部分——两者都消耗现有的 LOD 知识来理解表格中隐含的预期含义并产生新的事实和知识，这些事实和知识将成为网络的一部分。这代表公共语义数据的广度和深度显着增加，可以使“大数据”分析更加有效。