Formal representations of the visualization design space, such as knowledge bases and graphs, consolidate design practices into a shared resource and enable automated reasoning and interpretable design recommendations. However, prior approaches typically depend on fixed, manually authored rules, making it difficult to build novel representations or extend them for different visualization domains. Instead, we propose data-driven methods that automatically synthesize visualization design knowledge bases. Specifically, our methods (1) extract candidate design features from a visualization corpus, (2) select features forward and backward, and (3) render the final knowledge base. In our benchmark evaluation compared to Draco 2, our synthesized knowledge base offers general and interpretable design features and improves the accuracy of predicting effective designs by 1-15% in varied training and test sets. When we apply our approach to genomics visualization, the synthesized knowledge base includes sensible features with accuracy up to 97%, demonstrating the applicability of our approach to other visualization domains.

翻译：可视化设计空间的形式化表示（如知识库与知识图谱）将设计实践整合为共享资源，并支持自动化推理与可解释的设计推荐。然而，现有方法通常依赖于固定的人工编写规则，难以构建新颖的表示形式或将其扩展至不同的可视化领域。为此，我们提出数据驱动方法来自动合成可视化设计知识库。具体而言，我们的方法（1）从可视化语料库中提取候选设计特征，（2）通过前向与后向选择进行特征筛选，（3）生成最终知识库。在与Draco 2的基准评估对比中，我们合成的知识库提供了通用且可解释的设计特征，并在不同的训练集与测试集上将有效设计的预测准确率提升了1-15%。当我们将该方法应用于基因组学可视化时，合成的知识库包含了合理特征，准确率最高达97%，证明了本方法在其他可视化领域的适用性。