Digital Government: A Language-Modeling Approach To Metadata for Cross-Database Linkage and Search

EIA9983253 Callan Jamie Carnegie Mellon University数字政府：跨数据库链接和搜索的元数据的语言建模方法政府在线服务中的一个关键问题是，相关信息往往保存在不同机构的不同系统中，因此很难整合这些信息。在政府的“一站式购物”网站上，大多数尝试都因此而失败；它们必须手动维护，随着时间的推移，这是不起作用的。另一方面，由于政府持有的信息的分布式性质，数据仓库将无法工作，因为数据被清理、符合并存放在中央位置。解决这个问题的一个有希望的方法是使用自动化系统，该系统为各种数据集生成元数据(有关数据的数据)，使它们更容易集成，同时原始数据仍保留在其主站点。本提案的工作将以一种新的方式来解决这个问题--语言建模。政府的合作者是美国地质调查局、美国商务部、总务署和美国国会图书馆。

EIA9983253Callan JamieCarnegie Mellon UniversityDigital Government: A Language Modeling Approach to Metadata for Cross-Database Linkage and SearchA crucial problem in government on-line services is that related information is often maintained in different systems across different agencies, thus making it difficult to integrate that information. Most attempts at "one-stop shopping" sites in the government fail for that reason; they must be maintained manually which does not work over time. On the other hand, data warehousing, where the data is cleaned, made conformant and housed in a central location, will not work due to the distributed nature of government-held information.One promising avenue to approach this problem would be through the use of automated systems which generate metadata (data about data) for various datasets, allowing them to be integrated more easily, while the original data remains in place at its home site. The work of this proposal will address this problem in a new way - language modeling. The government collaborators are the US Geological Survey, the US Department of Commerce, the General Services Administration and the US Library of Congress.