Semantic Technology for Evidence Exploration and Learning (STEEL)

This paper describes an ontology driven framework for collaborative information analysis and knowledge discovery. The framework, called Semantic Technology for Evidence Exploration and Learning (STEEL), includes a collection of methods and an architecture that implements the method. The framework includes methods for collaborative visual analytics, workflow automation mechanisms, an automation support toolkit, and a semantic natural language processing pipeline. Application examples are described to illustrate the STEEL methods and automation support mechanisms. The main benefits of this research include (i) an improved ability to rapidly gain shared situational awareness through the use of collaborative visual analytics and information integration methods, (ii) significant reductions in information analysts' cognitive loads through the use of adaptive visualization methods, and (iii) a reduction in the time needed to generate decision-enabling information from large, multi-source data.