Overview

Manipulable Semantic Components (MSC) is a novel computational representation designed to support the generation, transformation, and analysis of expressive data visualizations from a scene-centric perspective. MSC provides high-level abstractions for both the structure of a visualization and the procedure to construct and modify the structure:

- The structure is described as semantic components such as mark, glyph, collection, layout, and encoding

- The procedure is expressed in terms of a series of operations that manipulate the semantic components, e.g., repeat, divide, densify, classify, and repopulate

MSC serves as the foundation for various applications in data visualization:

- VisAnatomy: a diverse SVG chart corpus with fine-grained semantic labels for AI and interactive applications

- Data Illustrateur: an authoring tool for creating expressive data visualizations without programming

- Mystique: deconstructing SVG charts for reusing complex chart layouts on user’s own dataset

Project Website

https://mascot-vis.github.io

Publications

VisAnatomy: An SVG Chart Corpus with Fine-Grained Semantic Labels, VIS 2025.
Chen Chen, Hannah K. Bako, Peihong Yu, John Hooker, Jeffrey Joyal, Simon C. Wang, Samuel Kim, Jessica Wu, Aoxue Ding, Lara Sandeep, Alex Chen, Chayanika Sinha, Zhicheng Liu

Manipulable Semantic Components: a Computational Representation of Data Visualization Scenes, VIS 2024
Zhicheng Liu, Chen Chen, John Hooker

Mystique: Deconstructing SVG Charts for Layout Reuse, VIS 2023
Chen Chen, Bongshin Lee, Yunhai Wang, Yunjeong Chang, Zhicheng Liu

Atlas: Grammar-based Procedural Generation of Data Visualizations, VIS 2021 (Short Paper)
Zhicheng Liu, Chen Chen, Francisco Morales, Yishan Zhao