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Query2Question: Translating Visualization Interaction into Natural Language.

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    This study introduces the query-to-question (Q2Q) system, which automatically records user interactions with visualization tools and translates them into natural language. This enhances data exploration by focusing on analytical reasoning and questioning.

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    Area of Science:

    • Data Visualization
    • Human-Computer Interaction
    • Natural Language Processing

    Background:

    • Interactive visualization tools are widely used for data exploration and analysis.
    • Current methods for recording user interactions are either too low-level or require manual transcription.
    • There is a need for automated, semantically rich provenance tracking in visualization.

    Purpose of the Study:

    • To present the architecture and translation design of the query-to-question (Q2Q) system.
    • To demonstrate how Q2Q automatically records and semantically represents user interactions in natural language.
    • To support a cross-examination process focused on analytical reasoning through questions.

    Main Methods:

    • Developed a query-to-question (Q2Q) system leveraging domain knowledge.
    • Employed natural language generation (NLG) techniques to translate visualization states into styled text.
    • Integrated Q2Q into various visualization tools for analysis across different knowledge domains.

    Main Results:

    • Q2Q automatically records user interactions and presents them semantically in written English.
    • The system generates a visual log of styled text, complementing visualization tool functionality.
    • Q2Q facilitates a question-focused analytic reasoning process.

    Conclusions:

    • The Q2Q system offers a novel approach to interaction provenance in data visualization.
    • It enhances data analysis by translating complex interactions into understandable natural language logs.
    • Q2Q supports deeper analytical reasoning by focusing on the 'why' behind user actions.