An effective visualization system depends on a user's ability to interpret a visual representation and made valid inferences. This paper first summarizes the role of domain knowledge when interpreting a visualization. Once the visual perception system has interpreted the visual representation, the user transforms the data into information by the introduction of domain knowledge; these are the rules or items of knowledge that are relevant tot this visual representation allowing the user to make meaningful inferences. In the remainder of the paper we concentrate on a visualization architecture that encapsulates domain knowledge to improve user interpretation of a visual representation. We use an agent-based paradigm to provide a distributed model of computation which moves away from a heavyweight constrained based algorithm towards a lightweight distributed system that empower individual data items. Finally, we present DIME, an implementation based on this approach. DIME is an ongoing research project that tightly integrates data storage, knowledge capture, and information visualization in a 'visual environment'.
"Improving visualization by capturing domain knowledge", Proc. SPIE 3960, Visual Data Exploration and Analysis VII, (28 February 2000); doi: 10.1117/12.378895; https://doi.org/10.1117/12.378895