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.

翻译：本研究基于对十九位知识图谱从业者的访谈，揭示其在企业及学术场景中多样化的应用实践。通过分析，我们识别出知识图谱创建、探索与分析过程中可通过可视化设计缓解的关键挑战。研究发现三类主要用户群体——知识图谱构建者、分析者与消费者，各自具备独特的专业需求与技能特征。构建者亟需模式强化工具，分析者需要可提供中间查询结果的自定义查询构建器，而消费者则对节点-连线图的低效性提出质疑，主张开发面向特定领域的定制化可视化方案以提升知识图谱的采纳度与可理解性。研究进一步表明，知识图谱的有效实践需要技术方案与社会协同的双重创新，现有工具、技术与协作流程尚未满足这些需求。基于访谈分析，我们提炼出多个提升知识图谱可用性的可视化研究方向：平衡可消化性与可发现性的知识卡片、追踪时序变化的时间轴视图、支持有机发现的交互界面，以及面向人工智能与机器学习预测的语义解释机制。