Open-world processes generate information that cannot be captured in a single data set. In fields such as medicine and defense, where precise information can be life-saving, a modeling paradigm is needed in which multiple media and contexts can be logically and visually integrated, in order to inform the engineering of large systems. One barrier is the underlying ontological heterogeneity that multiple contexts can exhibit, along with the need for those facts to be compatible with or translated between domains and situations. Another barrier is the dynamism and influence of context, which has traditionally been difficult to represent. This chapter describes a method for modeling the changes of interpretation that occur when facts cross-over context boundaries, whether those contexts are differentiated by discipline, time or perspective (or all three). We that processing Here, a new modeling environment is developed in which those transitions can be visualized. Our prototype modeling platform, Wunderkammer, can connect video, text, image and data while representing the context from which these artifacts were derived. It can also demonstrate transfers of information among situations, enabling the depiction of influence. Our example focuses on post-traumatic stress disorder (PTSD), combining psychological, neurological and physiological information, with a view to informing the aggregation of information in intelligent systems. These different forms of information are connected in a single modeling space using a narrative-based visual grammar. The goal is to develop a method and tool that supports the integration of information from different fields in order to model changing phenomena in an open world, with a focus on detecting emerging disorders. In turn, this will ultimately support more powerful knowledge systems for fields such as neurobiology, autonomous systems and artificial intelligence (AI).