Natural Language Interaction for Editing Visual Knowledge Graphs

TL;DR

This paper explores using natural language commands as an alternative to traditional GUI interactions for editing visual knowledge graphs, demonstrating significant effectiveness through empirical user studies.

Contribution

It introduces and empirically evaluates natural language interaction methods for graph editing, providing new insights into their advantages over traditional GUI approaches.

Findings

Natural language methods are significantly more effective than GUI interactions.

User study results show improved efficiency with natural language editing.

Natural language interfaces can reduce the tedium of multiple graph updates.

Abstract

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.