PNNL

Abstract

From our interactions with researchers from different scientific fields and disciplines, we have observed that scientists often describe and convey concepts, theories, processes, and results using basic graphs and diagrams. Semantic graphs such as these provide a universal language that all scientists may apply to document their scientific knowledge and to communicate this knowledge to others. Furthermore, studies have shown that the cognitive processing of complex subject matter is improved when the structure of ideas and concepts are made explicit [39] and that semantic graphs may serve as effective “scaffolds” for cognitive processing [29]. At Pacific Northwest National Laboratory, we are deploying semantic graphs within scientific computing systems as central user representations of scientific knowledge. These systems provide concept-based user interfaces that allow scientists to visually define and capture conceptual models of their scientific problems, hypotheses, theories, and processes. Once defined, the visual models then become interaction framework for accessing and applying scientific and computational resources and capabilities. In this paper, through the examination of three visual research systems, we illustrate different ways concept-based user interfaces and semantic graph knowledge representations may make scientific knowledge concrete, usable, shareable, and computable in scientific computing systems.