The primary goal of Visual Analytics (VA) is to enable user-guided knowledge generation. Theoretical VA works to explain how the different aspects of a VA tool bring forth new insights through user interactivity, which itself can be captured through tracking methods for reproduction or evaluation. However, the process of automatically capturing the user's thought process, such as intent and insights, and associating it with user's interaction events are 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 work, we propose Visual Analytics Knowledge Graph (VAKG), a conceptual framework that brings VA modeling theory to practice through a novel Set-Theory formalization of knowledge modeling. By extracting such a model from a VA tool, VAKG structures a 4-way temporal knowledge graph that describes user behavior and its associated knowledge gain process. Such knowledge graphs can be populated manually or automatically during user analysis sessions, which can then be analyzed using graph analysis methods. VAKG is demonstrated by modeling and collecting Tableau and visual text-mining workflows, where comparative user satisfaction, tool efficacy, and overall workflow shortcomings can be extracted from the knowledge graph.

翻译：可视化分析（VA）的首要目标是实现用户引导的知识生成。理论性VA研究致力于解释VA工具的不同方面如何通过用户交互催生新见解，而交互过程本身可通过追踪方法进行复现或评估。然而，自动捕获用户思维过程（如意图与见解）并将其与用户交互事件关联的机制往往被忽视。此外，两类交互捕获通常存在模糊混淆：时间维度体现事件序列，而非时间维度则将工作流解释为状态空间中的状态序列。本文提出可视化分析知识图谱（VAKG）这一概念框架，通过创新性的集合论知识建模形式化方法，将VA建模理论转化为实践。通过从VA工具中提取此类模型，VAKG构建了四维时序知识图谱，用以描述用户行为及其关联的知识获取过程。此类知识图谱可在用户分析会话期间通过人工或自动方式构建，进而采用图谱分析方法进行解析。我们通过建模并采集Tableau与可视化文本挖掘工作流验证了VAKG的效能，从知识图谱中可提取出用户满意度对比、工具效能评估及工作流整体缺陷。