EAGER: T2K: From Tables to Knowledge

网络让人类变得更聪明，提供了对大量知识和事实的便捷访问。语义网有能力通过让计算机程序和设备访问大量的数据、事实和知识来类似地增强它们。 该项目正在探索直接从电子表格、数据库关系和文档表中发现的数据自动提取新知识的可行性，并将其表示为语义Web语言RDF中的高度可互操作的链接开放数据（LOD）。 该提取是由概率图形模型，使用从当前的LOD知识资源挖掘的统计信息。 为了证明研究的潜在回报，该系统被用于从医学期刊收集的表格和来自data.gov等网站的表格中提取知识。虽然W3C语义Web语言RDF和OWL被用于表示知识，但结果适用于其他语义数据框架，如Microdata（搜索联盟），Freebase（谷歌），Probase（微软）和开放图（Facebook）。这个开源的原型软件允许其他研究人员尝试从表格中自动产生语义丰富的数据，如果成功的话，这样的软件提取系统有望成为新的在线知识生态的一部分-既消耗现有的LOD知识来理解表格中隐含的意图，又产生新的事实和知识，这些事实和知识将成为Web的一部分。这代表着公共语义数据的广度和深度的急剧增加，可以使“大数据”分析更加有效。

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.