Automated Discovery of Content-in-Context Relationships from a Large Corpus of Arctic Social Science Data

GSTDTAP

项目编号	1719540
	Automated Discovery of Content-in-Context Relationships from a Large Corpus of Arctic Social Science Data
	Peter Pulsifer
主持机构	University of Colorado at Boulder
项目开始年	2017
	2017-08-01
项目结束日期	2019-07-31
资助机构	US-NSF
项目类别	Continuing grant
项目经费	299947(USD)
国家	美国
语种	英语
英文摘要	This award is for an Early Concept Grant for Exploratory Research (EAGER) project research a potentially transformative approach to information management and knowledge discovery. The research will develop an approach to searching social science data (handwritten notes, analog and digital video, photos, and audio records, maps, illustrations, etc.) without associated metadata. The approach will be tested on a unique document collection generated from the Exchange for Local Observations and Knowledge of the Arctic (ELOKA), originally funded by the National Science Foundation during the International Polar Year 2007-2008. Leveraging inherent structures and boundary conditions among embedded levels of granularity in a digital resource collection (i.e., automated granularity), this project on Automated Discovery of Content-in-Context Relationships from a Large Corpus of Arctic Social Science Data has three objectives: (1) manage a document collection without metadata or databases: (2) generate relational schema without metadata or databases; and (3) analyze efficiencies and functionalities of automated granularity relative to metadata and database solutions. Files in Portable Document Format (PDF), which is a classic type of unstructured data, will be used to generate content-in-context relationships (i.e, relational schema) objectively and simply from the inherent structure of the documents.
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/71429
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Peter Pulsifer.Automated Discovery of Content-in-Context Relationships from a Large Corpus of Arctic Social Science Data.2017.