# KG4Vis: A Knowledge Graph-Based Approach for Visualization Recommendation

arXiv: https://arxiv.org/abs/2107.12548 · HTML: https://mlsanity.com/paper/2107.12548/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2021.

## Built on by (most cited first, 17 of 17)

- [Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning](https://mlsanity.com/paper/2407.20174/) (2024)
- [HAIChart: Human and AI Paired Visualization System](https://mlsanity.com/paper/2406.11033/) (2024)
- [LLM4Vis: Explainable Visualization Recommendation using ChatGPT](https://mlsanity.com/paper/2310.07652/) (2023)
- [Generative AI for Visualization: State of the Art and Future Directions](https://mlsanity.com/paper/2404.18144/) (2024)
- [AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making](https://mlsanity.com/paper/2312.04494/) (2023)
- [LightVA: Lightweight Visual Analytics with LLM Agent-Based Task Planning and Execution](https://mlsanity.com/paper/2411.05651/) (2024)
- [Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities](https://mlsanity.com/paper/2304.01311/) (2023)
- [ChartInsighter: An Approach for Mitigating Hallucination in Time-series Chart Summary Generation with A Benchmark Dataset](https://mlsanity.com/paper/2501.09349/) (2025)
- [LinkQ: An LLM-Assisted Visual Interface for Knowledge Graph Question-Answering](https://mlsanity.com/paper/2406.06621/) (2024)
- [Agnostic Visual Recommendation Systems: Open Challenges and Future Directions](https://mlsanity.com/paper/2302.00569/) (2023)
- [HealthGenie: Empowering Users with Healthy Dietary Guidance through Knowledge Graph and Large Language Models](https://mlsanity.com/paper/2504.14594/) (2025)
- [ChartKG: A Knowledge-Graph-Based Representation for Chart Images](https://mlsanity.com/paper/2410.09761/) (2024)
- [LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture](https://mlsanity.com/paper/2506.10347/) (2025)
- [The Role of Visualization in LLM-Assisted Knowledge Graph Systems: Effects on User Trust, Exploration, and Workflows](https://mlsanity.com/paper/2505.21512/) (2025)
- [Context-Mediated Domain Adaptation in Multi-Agent Sensemaking Systems](https://mlsanity.com/paper/2603.24858/) (2026)
- [Show Me the Infographic I Imagine: Intent-Aware Infographic Retrieval for Authoring Support](https://mlsanity.com/paper/2604.07989/) (2026)
- [Crystalis: Progressive Nucleation and Semantic Annealing for Coordinated Multi-View Visualization Generation](https://mlsanity.com/paper/2607.24766/) (2026)

## Abstract (arXiv)

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.