ChartKG: A Knowledge-Graph-Based Representation for Chart Images

• URL: http://arxiv.org/abs/2410.09761v1
• Date: Sun, 13 Oct 2024 07:38:44 GMT
• Title: ChartKG: A Knowledge-Graph-Based Representation for Chart Images
• Authors: Zhiguang Zhou, Haoxuan Wang, Zhengqing Zhao, Fengling Zheng, Yongheng Wang, Wei Chen, Yong Wang,
• Abstract summary: We propose a knowledge graph (KG) based representation for chart images, which can model the visual elements in a chart image and semantic relations among them. It integrates a series of image processing techniques to identify visual elements and relations, e.g., CNNs to classify charts, yolov5 and optical character recognition to parse charts. We present four cases to illustrate how our knowledge-graph-based representation can model the detailed visual elements and semantic relations in charts.
• Score: 9.781118203308438
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chart images, such as bar charts, pie charts, and line charts, are explosively produced due to the wide usage of data visualizations. Accordingly, knowledge mining from chart images is becoming increasingly important, which can benefit downstream tasks like chart retrieval and knowledge graph completion. However, existing methods for chart knowledge mining mainly focus on converting chart images into raw data and often ignore their visual encodings and semantic meanings, which can result in information loss for many downstream tasks. In this paper, we propose ChartKG, a novel knowledge graph (KG) based representation for chart images, which can model the visual elements in a chart image and semantic relations among them including visual encodings and visual insights in a unified manner. Further, we develop a general framework to convert chart images to the proposed KG-based representation. It integrates a series of image processing techniques to identify visual elements and relations, e.g., CNNs to classify charts, yolov5 and optical character recognition to parse charts, and rule-based methods to construct graphs. We present four cases to illustrate how our knowledge-graph-based representation can model the detailed visual elements and semantic relations in charts, and further demonstrate how our approach can benefit downstream applications such as semantic-aware chart retrieval and chart question answering. We also conduct quantitative evaluations to assess the two fundamental building blocks of our chart-to-KG framework, i.e., object recognition and optical character recognition. The results provide support for the usefulness and effectiveness of ChartKG.

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