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    We introduce KGScope, a new tool for exploring knowledge graphs. KGScope offers interactive visualizations and embedding-based guidance to help users discover insights within complex data networks.

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    Area of Science:

    • Computer Science
    • Data Science
    • Information Visualization

    Background:

    • Knowledge graphs are widely used for representing entity relationships and enhancing services.
    • Existing tools for knowledge graph exploration lack effectiveness due to unaddressed user needs and graph characteristics.
    • There is a need for specialized approaches to uncover and summarize insights within knowledge graphs.

    Purpose of the Study:

    • To propose KGScope, a novel system for interactive visual exploration of knowledge graphs.
    • To provide embedding-based guidance for deriving insights from knowledge graphs.
    • To enhance the effectiveness of knowledge graph exploration for data analysts and researchers.

    Main Methods:

    • Developed KGScope with interactive visual exploration capabilities.
    • Integrated embedding-based guidance to assist in insight discovery.
    • Conducted usage scenarios and a user study to evaluate KGScope's efficacy.

    Main Results:

    • KGScope effectively supports knowledge graph exploration.
    • The system provides valuable information for users.
    • KGScope aids in exploring the entire knowledge graph network.

    Conclusions:

    • KGScope enhances the process of deriving insights from knowledge graphs.
    • Interactive visualization and embedding-based guidance are crucial for effective exploration.
    • The proposed system demonstrates significant utility in understanding complex knowledge graph data.