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

该奖项授予探索性研究（EAGER）项目的早期概念资助，用于研究信息管理和知识发现的潜在变革方法。该研究将开发一种无需关联元数据的搜索社会科学数据（手写笔记、模拟和数字视频、照片和音频记录、地图、插图等）的方法。该方法将在一个独特的文献收集上进行测试，该文献收集来自北极地方观测和知识交流项目（ELOKA），该项目最初是由美国国家科学基金会在2007-2008年国际极地年期间资助的。利用数字资源集合（即自动化粒度）中嵌入粒度级别之间的固有结构和边界条件，这个关于从北极社会科学数据大型语料库中自动发现上下文中的内容关系的项目有三个目标：(1)管理没有元数据或数据库的文档集合；(2)生成没有元数据或数据库的关系模式；(3)分析相对于元数据和数据库解决方案的自动化粒度的效率和功能。可移植文档格式（Portable Document Format， PDF）文件是一种经典的非结构化数据类型，将用于客观而简单地从文档的固有结构中生成上下文内容关系（即关系模式）。

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.