A Theoretical Approach for Structuring and Analysing Knowledge Provenance for Visual Analytics

Abstract The primary goal of visual analytics (VA) is to enable user‐guided knowledge generation. Theoretical VA aims to explain how the different aspects of a VA tool yield new insights through user interactivity, which itself can be captured using tracking methods for reproduction or evaluation. However, the strategy of automatically capturing the user's thought processes, such as intent and insights, and associating them with user interaction events is largely ignored. Also, two forms of interactivity capture are typically ambiguous and intermixed: the temporal aspect, which indicates sequences of events, and the atemporal aspect, which explains the workflow as sequences of states within a state‐space . In this article, we propose the visual analytics knowledge graph (VAKG), a conceptual framework that brings VA modelling theory to practice through a novel set‐theory formalization of knowledge modelling. By extracting such a model from a VA tool, VAKG constructs a 4‐way temporal knowledge graph that describes user behaviour and the associated knowledge‐gain process. Such knowledge graphs can be populated manually or automatically during user analytical sessions, and then analysed using graph‐based methods. VAKG is demonstrated by modelling and collecting Tableau and visual text‐mining workflows, enabling the extraction of comparative user satisfaction, tool efficacy, and overall workflow shortcomings from the produced knowledge graph.