From Data to Knowledge Graphs: A Multi-Layered Method to Model User's Visual Analytics Workflow for Analytical Purposes
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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.
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