Year: 2024
Type: conference-paper
Abstract: Information in the ORKG is mostly derived from text in the scientific articles. However, it does not utilize the information in the figures. Inferring insights from the charts is challenging: Chart figures contain text and visual elements to convey meaning through visual encoding and representation of data such as points, lines, bars etc. In related studies, there is a lack of research in context and domain-aware chart comprehension. Our objective is to address these challenges, utilizing state-of-the-art vision-language models. The envisioned results will enhance scientific communication, for instance making the contents of scientific visualizations accessible to visually impaired readers; and contribute to the development of the ORKG leading to more comprehensive listings of paper contributions and comparisons. Finally, the ORKGAsk could be improved viachart-based insights, with references to the figures in question, surrounding text paragraphs and additional domain knowledge.
Authors: Ewerth, Ralph, Ahmed, Fahad, Müller-Budack, Eric, Hoppe, Anett, Auer, Sören
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6948112779
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