research-article

GVQA: Learning to Answer Questions about Graphs with Visualizations via Knowledge Base

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Changbo WangAuthors Info & Claims

CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Article No.: 464, Pages 1 - 16

https://doi.org/10.1145/3544548.3581067

Published: 19 April 2023 Publication History

Abstract

Graphs are common charts used to represent the topological relationship between nodes. It is a powerful tool for data analysis and information retrieval tasks involve asking questions about graphs. In formative study, we found that questions for graphs are not only about the relationship of nodes but also about the properties of graph elements. We propose a pipeline to answer natural language questions about graph visualizations and generate visual answers. We first extract the data from graphs and convert them into GML format. We design data structures to encode graph information and convert them into an knowledge base. We then extract topic entities from questions. We feed questions, entities and knowledge bases into our question-answer model to obtain the SPARQL queries for textual answers. Finally, we design a module to present the answers visually. A user study demonstrates that these visual and textual answers are useful, credible and and transparent.

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GVQA: Learning to Answer Questions about Graphs with Visualizations via Knowledge Base

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cover image ACM Conferences

CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

April 2023

14911 pages

ISBN:9781450394215

DOI:10.1145/3544548

Editors:
Albrecht Schmidt
LMU Munich, Germany60028717
,
Kaisa Väänänen
Tampere University, Finland60011170
,
Tesh Goyal
Google Research, USA60006191
,
Per Ola Kristensson
University of Cambridge, UK60031101
,
Anicia Peters
University of Namibia, Namibia60072704
,
Stefanie Mueller
Massachusetts Institute of Technology, USA60022195
,
Julie R. Williamson
University of Glasgow, UK60001490
,
Max L. Wilson
University of Nottingham, UK60015138

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Published: 19 April 2023

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https://doi.org/10.1109/TVCG.2024.3456364
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