@inproceedings{f85436ae60c54c0f9af27f366ebf1640,
title = "GVQA: Learning to Answer Questions about Graphs with Visualizations via Knowledge Base",
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.",
keywords = "Knowledge Base, Natural Language Process, Network Graph, Question Answering, Reinforcement Learning, Visualization",
author = "Sicheng Song and Juntong Chen and Chenhui Li and Changbo Wang",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 2023 CHI Conference on Human Factors in Computing Systems, CHI 2023 ; Conference date: 23-04-2023 Through 28-04-2023",
year = "2023",
month = apr,
day = "19",
doi = "10.1145/3544548.3581067",
language = "英语",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "Association for Computing Machinery",
booktitle = "CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems",
address = "美国",
}