The ability to identify, process, and comprehend the essential elements of information associated with a given operational environment can be used to reason about how the actors within the environment can best respond. This is often referred to as 'situation assessment,' the end state of which is 'situation awareness,' which can be simply defined as 'knowing what is going on around you.' Taken together, these are important fields of study concerned with perception of the environment critical to decision-makers in many complex, dynamic domains, including aviation, military command and control, and emergency management. The primary goal of our research is to identify some of the main technical challenges associated with automated situation assessment, in general, and to propose an information processing methodology that meets those challenges, which we call Find-to-Forecast (F2F). The F2F framework supports accessing heterogeneous information (structured and unstructured), which is normalized into a standard RDF representation. Next, the F2F framework identifies mission-relevant information elements, filtering out irrelevant (or low priority) information, fusing the remaining relevant information. The next steps in the F2F process involve focusing operator attention on essential elements of mission information, and reasoning over fused, relevant information to forecast potential courses of action based on the evolving situation, changing data, and uncertain knowledge. This paper provides an overview of the overall F2F methodology, to provide context, followed by a more detailed consideration of the 'focus' algorithm, which uses contextual semantics to evaluate the value of new information relative to an operator's situational understanding during evolving events.