Visual Analytics for Understanding Draco's Knowledge Base
Johanna Schmidt, Bernhard Pointner, Silvia Miksch

TL;DR
This paper introduces a Visual Analytics approach to visualize and analyze Draco's knowledge base, helping experts understand complex constraints and recommendation decisions in an automated visualization system.
Contribution
It extends Draco's data extraction with a new architecture and visualization method using hypergraphs, improving interpretability of its logical constraints.
Findings
Prototype effectively visualizes Draco's constraints and violations.
Evaluation confirms improved understanding of Draco's recommendation process.
Supports interactive exploration of design rules and violations.
Abstract
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…
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Taxonomy
TopicsSemantic Web and Ontologies · Constraint Satisfaction and Optimization · Logic, Reasoning, and Knowledge
