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

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    This study introduces ChartKG, a new knowledge graph (KG) representation for chart images. ChartKG captures visual elements and semantic relationships, improving chart knowledge mining and downstream applications.

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    Area of Science:

    • Computer Science
    • Data Visualization
    • Artificial Intelligence

    Background:

    • Chart images are increasingly prevalent, necessitating advanced knowledge mining techniques.
    • Current methods often lose information by focusing solely on raw data extraction.
    • Visual encodings and semantic meanings in charts are frequently overlooked.

    Purpose of the Study:

    • To propose ChartKG, a novel knowledge graph (KG) representation for chart images.
    • To develop a framework for converting chart images into this KG-based representation.
    • To enhance downstream tasks like chart retrieval and question answering through richer chart understanding.

    Main Methods:

    • Utilized Convolutional Neural Networks (CNNs) for chart classification.
    • Employed YOLOv5 and Optical Character Recognition (OCR) for chart parsing.
    • Implemented rule-based methods for constructing knowledge graphs from chart elements.

    Main Results:

    • Demonstrated ChartKG's ability to model visual elements and semantic relations within charts.
    • Showcased benefits for semantic-aware chart retrieval and chart question answering.
    • Validated the effectiveness of object recognition and OCR components in the chart-to-KG framework.

    Conclusions:

    • ChartKG offers a unified representation for visual elements and semantic relations in chart images.
    • The proposed framework effectively converts chart images into a KG-based format.
    • ChartKG significantly enhances the performance of downstream chart-related applications.