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：本研究通过访谈十九位在企业与学术环境中从事多种用例的知识图谱（KG）实践者，揭示了其认知洞见。研究识别出实践者在创建、探索与分析KG过程中面临的关键挑战，这些挑战可通过可视化设计得以缓解。研究发现KG实践者存在三种主要角色画像——KG构建者、分析师与消费者，每类角色均具备独特专长与需求。我们指出：KG构建者将从模式强化工具中获益，KG分析师需要能提供中间查询结果的可定制查询构建器，而KG消费者层面则发现节点-链接图存在效能不足问题，亟需定制化的领域特异性可视化以促进KG采纳与理解。此外，研究揭示在实践中有效实施KG需要当前工具、技术与协作工作流未能满足的技术与社会双重解决方案。基于访谈分析，我们提炼出若干提升KG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、追踪时间变化的时序视图、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。