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（KG）从业者的访谈，他们分别在企业和学术环境中处理多种用例。通过这项研究，我们识别出KG从业者在创建、探索和分析知识图谱时面临的关键挑战，这些挑战可通过可视化设计得到缓解。我们的发现揭示了KG从业者的三种主要角色——KG构建者、分析者和使用者——每个角色都有其独特的专业知识和需求。我们发现，KG构建者可从模式强制执行工具中受益，而KG分析者需要可定制化的查询构建器，以提供中间查询结果。对于KG使用者，我们识别出节点连接图的有效性不足，并需要针对特定领域的定制化可视化来促进知识图谱的采用和理解。最后，我们发现在实践中有效实施知识图谱需要技术和社交两方面的解决方案，而当前工具、技术和协作工作流未能解决这些问题。通过对访谈的分析，我们提炼出几个提升知识图谱可用性的可视化研究方向，包括平衡可消化性与可发现性的知识卡片、追踪时间演变的时序视图、支持有机发现的交互界面，以及针对AI和机器学习预测的语义解释。