Many web-based visualizations are deployed as Scalable Vector Graphics (SVG), a format that faithfully preserves visual appearance but typically omits the higher-level semantic structure needed for machine interpretation. Once rendered and published, information about a visualization's components, roles, and encodings is no longer explicitly available, limiting downstream operations such as querying, accessibility augmentation, explanation, personalization, and transformation. To address this gap, we introduce CSL, an AI-enabled, multi-stage pipeline for automatically recovering visualization semantics from deployed SVGs through two complementary mechanisms: (1) cohort-based decomposition, which organizes heterogeneous SVG primitives into structurally coherent subsets that reduce the semantic assignment space, and (2) hybrid semantic grounding, which combines model-based inference with deterministic structural validation and propagation to make labeling both context-sensitive and structurally anchored. CSL produces Semantic SVG (SSVG), a representation in which SVG elements are annotated with graphical mark type, visualization role, and data role. We implemented CSL as an end-to-end prototype and evaluated it on 102 SVG visualizations, achieving global macro-averaged accuracies of 0.822 for mark type, 0.853 for visualization role, and 0.860 for data-role recovery. An ablation against a non-cohort whole-chart baseline showed that cohorting significantly improves accuracy (paired t-test: t > 20, p < 0.001; Cohen's d > 2.0), and repeated labeling of a randomly selected SVG over 100 runs yielded mean agreement above 91.9% across all three attributes. These results provide strong evidence that CSL can transform deployed SVGs into machine-usable semantic representations, enabling more accessible, adaptive, and user-steerable visualization systems.

翻译：许多基于网页的可视化以可缩放矢量图形（SVG）形式部署，这种格式忠实地保留了视觉外观，但通常省略了机器理解所需的高级语义结构。一旦渲染并发布，关于可视化各组件、角色和编码的信息便不再显式可用，限制了查询、无障碍增强、解释、个性化及转换等下游操作。为解决这一缺陷，我们提出CSL——一种基于AI的多阶段流水线，通过两种互补机制从已部署SVG中自动恢复可视化语义：（1）基于集群的分解，将异构SVG图元组织为结构连贯的子集，以缩减语义赋值空间；（2）混合语义锚定，结合基于模型的推理与确定性结构验证及传播，使标注兼具上下文敏感性和结构锚定性。CSL生成语义SVG（SSVG）——一种用图形标记类型、可视化角色和数据角色对SVG元素进行注释的表示形式。我们将CSL实现为端到端原型，并在102个SVG可视化上评估，实现了全局宏平均准确率：标记类型0.822、可视化角色0.853、数据角色恢复0.860。与无集群的全图表基线的消融实验表明，集群化显著提升了准确率（配对t检验：t > 20，p < 0.001；Cohen's d > 2.0），且对随机选取的SVG进行100次重复标注后，三个属性的平均一致性均超过91.9%。这些结果有力证明了CSL能将已部署SVG转化为机器可用的语义表示，从而实现更易访问、更自适应、更用户可控的可视化系统。