  Visualization recommendation or automatic visualization generation can
significantly lower the barriers for general users to rapidly create effective
data visualizations, especially for those users without a background in data
visualizations. However, existing rule-based approaches require tedious manual
specifications of visualization rules by visualization experts. Other machine
learning-based approaches often work like black-box and are difficult to
understand why a specific visualization is recommended, limiting the wider
adoption of these approaches. This paper fills the gap by presenting KG4Vis, a
knowledge graph (KG)-based approach for visualization recommendation. It does
not require manual specifications of visualization rules and can also guarantee
good explainability. Specifically, we propose a framework for building
knowledge graphs, consisting of three types of entities (i.e., data features,
data columns and visualization design choices) and the relations between them,
to model the mapping rules between data and effective visualizations. A
TransE-based embedding technique is employed to learn the embeddings of both
entities and relations of the knowledge graph from existing
dataset-visualization pairs. Such embeddings intrinsically model the desirable
visualization rules. Then, given a new dataset, effective visualizations can be
inferred from the knowledge graph with semantically meaningful rules. We
conducted extensive evaluations to assess the proposed approach, including
quantitative comparisons, case studies and expert interviews. The results
demonstrate the effectiveness of our approach.
