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