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NameClarifier: A Visual Analytics System for Author Name Disambiguation.

Qiaomu Shen, Tongshuang Wu, Haiyan Yang

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    Summary

    NameClarifier interactively disambiguates author names using co-authorships, venues, and time. This human-in-the-loop system improves accuracy for complex cases in digital libraries.

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    Area of Science:

    • Information Science
    • Computer Science
    • Bibliometrics

    Background:

    • Accurate author name disambiguation is crucial for bibliometric analysis and research integrity.
    • Traditional data mining models struggle with highly ambiguous author names in digital libraries.
    • Existing disambiguation methods often lack transparency and user interaction.

    Purpose of the Study:

    • To introduce NameClarifier, a novel visual analytics system for interactive author name disambiguation.
    • To quantify and visualize name similarities using co-authorships, publication venues, and temporal information.
    • To enhance the reliability and transparency of author name disambiguation processes.

    Main Methods:

    • Developed a visual analytics system (NameClarifier) that quantifies name similarities.
    • Utilized co-authorships, publication venues, and temporal data as key similarity factors.
    • Incorporated a human-in-the-loop approach for interactive validation of ambiguous cases.

    Main Results:

    • NameClarifier effectively quantifies and visualizes similarities between ambiguous and confirmed author names.
    • The system provides visual cues to aid users in validating disambiguation cases.
    • Interactive disambiguation achieved more reliable results for highly ambiguous cases compared to general models.
    • Resolved disambiguation cases were fed back into the system, improving future accuracy.

    Conclusions:

    • NameClarifier offers an effective and transparent solution for author name disambiguation.
    • The human-in-the-loop approach enhances the reliability and interpretability of disambiguation results.
    • The system's ability to learn from resolved cases offers a dynamic and improving disambiguation process.