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research-article

Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities

Authors:

Gabriel Appleby,

Camelia Daniela Brumar,

Ashley SuhAuthors Info & Claims

IEEE Transactions on Visualization and Computer Graphics, Volume 30, Issue 1

Pages 584 - 594

https://doi.org/10.1109/TVCG.2023.3326904

Published: 01 January 2024 Publication History

Abstract

This study presents insights from interviews with nineteen Knowledge Graph (KG) practitioners who work in both enterprise and academic settings on a wide variety of use cases. Through this study, we identify critical challenges experienced by KG practitioners when creating, exploring, and analyzing KGs that could be alleviated through visualization design. Our findings reveal three major personas among KG practitioners – KG Builders, Analysts, and Consumers – each of whom have their own distinct expertise and needs. We discover that KG Builders would benefit from schema enforcers, while KG Analysts need customizable query builders that provide interim query results. For KG Consumers, we identify a lack of efficacy for node-link diagrams, and the need for tailored domain-specific visualizations to promote KG adoption and comprehension. Lastly, we find that implementing KGs effectively in practice requires both technical and social solutions that are not addressed with current tools, technologies, and collaborative workflows. From the analysis of our interviews, we distill several visualization research directions to improve KG usability, including knowledge cards that balance digestibility and discoverability, timeline views to track temporal changes, interfaces that support organic discovery, and semantic explanations for AI and machine learning predictions.

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Cited By

Afreen NBalloccu GBoratto LFenu GMalloci FMarras MMartis ABoratto LGena CMarras MGermanakos PPopescus E(2024)Learner-centered Ontology for Explainable Educational RecommendationAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665226(567-575)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665226
Hsuan Yuan CYu TPan JLin W(2024)KGScope: Interactive Visual Exploration of Knowledge Graphs With Embedding-Based GuidanceIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.336069030:12(7702-7716)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3360690

Index Terms

Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities
1. Human-centered computing
2. Information systems

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Visualization and Computer Graphics

IEEE Transactions on Visualization and Computer Graphics Volume 30, Issue 1

Jan. 2024

1456 pages

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1077-2626 © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 January 2024

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Cited By

Afreen NBalloccu GBoratto LFenu GMalloci FMarras MMartis ABoratto LGena CMarras MGermanakos PPopescus E(2024)Learner-centered Ontology for Explainable Educational RecommendationAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665226(567-575)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665226
Hsuan Yuan CYu TPan JLin W(2024)KGScope: Interactive Visual Exploration of Knowledge Graphs With Embedding-Based GuidanceIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.336069030:12(7702-7716)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3360690

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