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

从大量北极社会科学数据中自动发现内容上下文关系

基本信息

批准号：
1719540
负责人：
Peter Pulsifer
金额：
$ 29.99万
依托单位：
University of Colorado at Boulder
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1719540&HistoricalAwards=false
关键词：
Automated Discovery Content Context Relationships

项目摘要

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.

该奖项是为探索性研究（急切）项目研究的早期概念赠款提供的一种潜在的信息管理和知识发现方法。该研究将开发一种搜索社会科学数据的方法（手写笔记，模拟和数字视频，照片和音频记录，地图，插图等），而无需关联的元数据。该方法将对来自国际科学基金会在2007 - 2008年国际极地基金会资助的北极（ELOKA）的当地观察和知识（ELOKA）产生的独特文档收集进行测试。在数字资源收集（即自动化粒度）中，在嵌入式粒度层次之间的固有结构和边界条件，该项目从北极社会科学数据的大量语料库中自动化发现内容之间的关系有三个目标：（1）没有元数据或数据库中的文档，而没有元数据库：（2）无元数据，而无需构成关系。（3）分析相对于元数据和数据库解决方案，自动粒度的效率和功能。便携式文档格式（PDF）的文件是一种经典的非结构化数据类型，将用于客观地，简单地从文档的固有结构中生成contement-intext下文关系（即关系架构）。