ChartMark: A Structured Grammar for Chart Annotation

2025-07-29

AI Summary

Current chart annotations lack a unified semantic representation, exhibiting fragmentation and non-standardization that severely hinder cross-platform reusability and accessibility enhancement. To address this, we propose ChartMark—a structured, task-driven domain-specific language (DSL) for chart annotation—that decouples semantic content from visualization implementation, enabling hierarchical expression from abstract analytical intent to concrete visual details. Built upon the Vega-Lite grammar, ChartMark features an automated translation mechanism that supports end-to-end generation of accessible visualizations directly from annotation specifications. Empirical evaluation via an integrated toolchain demonstrates that ChartMark significantly improves annotation expressivity, cross-platform compatibility, and developer productivity. It establishes a scalable, formally verifiable semantic infrastructure for accessible data visualization, advancing interoperability and inclusivity in visual analytics systems.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Computer Vision: Visual Reasoning & Symbolic Representations
• Humans and AI: AI for Accessibility

Application Category

• Semantics and Knowledge: Representation, semantic annotation, enhancement, enrichments, access and/or integration of a variety of data on the Web
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Web Mining and Content Analysis: Web data visualization

Abstract

Chart annotations enhance visualization accessibility but suffer from fragmented, non-standardized representations that limit cross-platform reuse. We propose ChartMark, a structured grammar that separates annotation semantics from visualization implementations. ChartMark features a hierarchical framework mapping onto annotation dimensions (e.g., task, chart context), supporting both abstract intents and precise visual details. Our toolkit demonstrates converting ChartMark specifications into Vega-Lite visualizations, highlighting its flexibility, expressiveness, and practical applicability.

Problem

• Standardizing fragmented chart annotation representations
• Separating annotation semantics from visualization implementations
• Enabling cross-platform reuse of chart annotations

Innovation

• Structured grammar separates annotation semantics
• Hierarchical framework maps annotation dimensions
• Toolkit converts specs to Vega-Lite visualizations