用于理解 Draco 知识库的可视化分析
Visual Analytics for Understanding Draco's Knowledge Base
AI总结：
针对 Draco 知识库约束增多导致推荐决策难以追溯的问题，提出基于修订 ASP 语法与超图的可视化分析方法，帮助专家交互式探索约束并验证其有效性。
摘要：
Draco 已被开发为一个自动化可视化推荐系统，它将设计知识形式化为 ASP（Answer-Set Programming，回答集编程）中的逻辑约束。随着约束集合及所纳入设计知识的不断增加，即使是可视化专家也会失去对 Draco 的全局认识，并难以追溯系统所做出的自动化推荐决策。本文提出一种 Visual Analytics（VA，可视化分析）方法，用于可视化和分析 Draco 的约束。该 VA 方法旨在使可视化专家能够完成已识别出的与知识库相关的任务，并帮助他们更好地理解 Draco。我们扩展了 Draco 现有的数据提取策略，加入了一种能够从知识库中提取感兴趣特征的数据处理架构。修订版 ASP 语法为这一数据处理策略提供了基础。随后，所得出的约束的共有特征和共享特征，在精心设计的可视化中通过径向排列的约束内部的超图结构进行可视化。约束的层次类别由围绕这些约束的弧形表示。我们的方法旨在使可视化专家能够通过高亮相应约束或推荐，交互式地探索设计规则的违反情况。对原型的定性与定量评估证实，该原型在深入理解 Draco 的推荐过程和设计约束方面具有有效性和价值。
英文摘要：
Draco has been developed as an automated visualization recommendation system formalizing design knowledge as logical constraints in ASP (Answer-Set Programming). With an increasing set of constraints and incorporated design knowledge, even visualization experts lose overview in Draco and struggle to retrace the automated recommendation decisions made by the system. Our paper proposes an Visual Analytics (VA) approach to visualize and analyze Draco's constraints. Our VA approach is supposed to enable visualization experts to accomplish identified tasks regarding the knowledge base and support them in better understanding Draco. We extend the existing data extraction strategy of Draco with a data processing architecture capable of extracting features of interest from the knowledge base. A revised version of the ASP grammar provides the basis for this data processing strategy. The resulting incorporated and shared features of the constraints are then visualized using a hypergraph structure inside the radial-arranged constraints of the elaborated visualization. The hierarchical categories of the constraints are indicated by arcs surrounding the constraints. Our approach is supposed to enable visualization experts to interactively explore the design rules' violations based on highlighting respective constraints or recommendations. A qualitative and quantitative evaluation of the prototype confirms the prototype's effectiveness and value in acquiring insights into Draco's recommendation process and design constraints.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
