Structured Visualization Design Knowledge for Grounding Generative Reasoning and Situated Feedback

Abstract

Automated visualization design navigates a tension between symbolic systems and generative models. Constraint solvers enforce structural and perceptual validity, but the rules they require are difficult to author and too rigid to capture situated design knowledge. Large language models require no formal rules and can reason about contextual nuance, but they prioritize popular conventions over empirically grounded best practices. We address this tension by proposing a cataloging scheme that structures visualization design knowledge as natural-language guidelines with semantically typed metadata. This allows experts to author knowledge that machines can query. An expert study ($N=18$) indicates that practitioners routinely adapt heuristics to situational factors such as audience and communicative intent. To capture this reasoning, guideline sections specify not only advice but also the contexts where it applies, exceptions that invalidate it, and the sources from which it derives. We demonstrate the scheme's expressiveness by cataloging 744 guidelines drawn from cognitive science, accessibility standards, data journalism, and research on rhetorical aspects of visual communication. We embed guideline sections in a vector space, opening the knowledge itself to structural analysis. This reveals conflicting advice across sources and transferable principles between domains. Rather than replacing constraint-based tools, our scheme provides what they lack: situated guidance that generative systems can retrieve to ground their reasoning, users can verify against cited sources, and experts can author as knowledge evolves.