Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities
Harry X. Li, Gabriel Appleby, Camelia Daniela Brumar, Remco Chang, Ashley Suh
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext bcad3440-3bc5-4123-b2b8-2f1e9f99597fCited by top-tier papers2
- From Genesis to Maturity: Managing Knowledge Graph Ecosystems Through Life CyclesSandra Geisler, Cinzia Cappiello, Irene Celino, David Chaves-Fraga et al.VLDB 2025 · 4 citations
- DiagLink: A Dual-User Diagnostic Assistance System by Synergizing Experts with LLMs and Knowledge GraphsZihan Zhou, Yinan Liu, Yuyang Xie, Bin Wang et al.CHI 2026 · 1 citation
Builds on8
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah et al.NeurIPS 2020 · 64,255 citations
- "Everyone wants to do the model work, not the data work": Data Cascades in High-Stakes AINithya Sambasivan, Shivani Kapania, Hannah Highfill, Diana Akrong et al.CHI 2021 · 725 citations
- NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language QueriesArpit Narechania, Arjun Srinivasan, John T. StaskoIEEE VIS 2020 · 210 citations
- Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their NeedsHarini Suresh, Steven R. Gomez, Kevin K. Nam, Arvind SatyanarayanCHI 2021 · 115 citations
- KG4Vis: A Knowledge Graph-Based Approach for Visualization RecommendationHaotian Li, Yong Wang, Songheng Zhang, Yangqiu Song et al.IEEE VIS 2021 · 111 citations
Related papers
- Envisage: Towards Expressive Visual Graph QueryingXiaolin Wen, Qishuang Fu, Shuangyue Han, Yichen Guo et al.IEEE VIS 2025 · 6 citations
- KNowNEt:Guided Health Information Seeking from LLMs via Knowledge Graph IntegrationYoufu Yan, Yu Hou, Yongkang Xiao, Rui Zhang et al.IEEE VIS 2024 · 33 citations
- AutoML in The Wild: Obstacles, Workarounds, and ExpectationsYuan Sun, Qiurong Song, Xinning Gui, Fenglong Ma et al.CHI 2023 · 28 citations
- Human Factors in Model Interpretability: Industry Practices, Challenges, and NeedsSungsoo Ray Hong, Jessica Hullman, Enrico BertiniCSCW 2020 · 219 citations
- Generative Knowledge Graph Construction: A ReviewHongbin Ye, Ningyu Zhang, Hui Chen, Huajun ChenEMNLP 2022 · 51 citations
