Natural Language Interaction for Editing Visual Knowledge GraphsReza Shahriari, Eric D. Ragan, and Jaime Ruiz

Reza Shahriari, Eric D. Ragan, and Jaime Ruiz. 2025. Natural Language Interaction for Editing Visual Knowledge Graphs. In Proceedings of the 13th Knowledge Capture Conference 2025 (K-CAP ’25). Association for Computing Machinery, New York, NY, USA, 26–34. https://doi.org/10.1145/3731443.3771344

@inproceedings{10.1145/3731443.3771344,
author = {Shahriari, Reza and Ragan, Eric D. and Ruiz, Jaime},
title = {Natural Language Interaction for Editing Visual Knowledge Graphs},
year = {2025},
isbn = {9798400718670},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3731443.3771344},
doi = {10.1145/3731443.3771344},
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.},
booktitle = {Proceedings of the 13th Knowledge Capture Conference 2025},
pages = {26–34},
numpages = {9},
keywords = {Knowledge Capture and Interaction, Natural Language Interfaces},
location = {
},
series = {K-CAP '25}
}