A Semi-Automated Interpretation-Based Network Construction Tool (SINC-Tool)

While existing tools such as Carnegie Mellon"s Organizational Risk Assessment (ORA) system provide ways to conduct rapid meta-data social network analysis by automatically extracting information from data and performing predictive analysis, purely automated approaches to network construction suffer from several key functional deficits: they often ingest intelligence into a limited set of data representations and do not cope with uncertainty; they do not prioritize automated output based the operational needs of the user for actionable intelligence; and they leave the end user without a way to properly influence the structure and value of additions to social networks. To support rapid meta-data social network construction through machine-assisted interpretation of intelligence, we propose to design and demonstrate a Semi-Automated Interpretation-Based Network Construction Tool (SINC-Tool). This effort can be summarized in three major thrusts: we will design and demonstrate a novel computational knowledge representation to capture interpretations with inherent uncertainty; we will demonstrate a prioritization calculation that enables the efficient organization and rapid selection of interpreted information based on the Analyst"s operational mission; and we will perform a work domain analysis and design a software tool to support semi-automated intelligence interpretation that can interface to existing social network analysis software.