The importance of knowledge generation drives much of Visual Analytics (VA).
User-tracking and behavior graphs have shown the value of understanding users'
knowledge generation while performing VA workflows. Works in theoretical
models, ontologies, and provenance analysis have greatly described means to
structure and understand the connection between knowledge generation and VA
workflows. Yet, two concepts are typically intermixed: the temporal aspect,
which indicates sequences of events, and the atemporal aspect, which indicates
the workflow state space. In works where these concepts are separated, they do
not discuss how to analyze the recorded user's knowledge gathering process when
compared to the VA workflow itself. This paper presents Visual Analytic
Knowledge Graph (VAKG), a conceptual framework that generalizes existing
knowledge models and ontologies by focusing on how humans relate to computer
processes temporally and how it relates to the workflow's state space. Our
proposal structures this relationship as a 4-way temporal knowledge graph with
specific emphasis on modeling the human and computer aspect of VA as separate
but interconnected graphs for, among others, analytical purposes. We compare
VAKG with relevant literature to show that VAKG's contribution allows VA
applications to use it as a provenance model and a state space graph, allowing
for analytics of domain-specific processes, usage patterns, and users'
knowledge gain performance. We also interviewed two domain experts to check, in
the wild, whether real practice and our contributions are aligned.Comment: 9 pgs, submitted to VIS 202