# Natural Language Interaction for Editing Visual Knowledge Graphs

arXiv: https://arxiv.org/abs/2512.11674 · HTML: https://mlsanity.com/paper/2512.11674/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2025.

## Built on by (most cited first, 1 of 1)

- [MindTrellis: Co-Creating Knowledge Structures with AI through Interactive Visual Exploration](https://mlsanity.com/paper/2604.23129/) (2026)

## Abstract (arXiv)

Knowledge graphs are often visualized using node-link diagrams that reveal relationships and structure. In many applications using graphs, it is desirable to allow users to edit graphs to ensure data accuracy or provides updates. Commonly in graph visualization, users can interact directly with the visual elements by clicking and typing updates to specific items through traditional interaction methods in the graphical user interface. However, it can become tedious to make many updates due to the need to individually select and change numerous items in a graph. Our research investigates natural language input as an alternative method for editing network graphs. We present a user study comparing GUI graph editing with two natural language alternatives to contribute novel empirical data of the trade-offs of the different interaction methods. The findings show natural language methods to be significantly more effective than traditional GUI interaction.