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

TL;DR

The paper investigates how real-world KG practitioners use, query, and visualize knowledge graphs, uncovering three user personas (Builders, Analysts, Consumers) and a range of domain-specific needs. Through 19 interviews, the authors characterize use cases, tools, data-quality and socio-technical challenges, and the limitations of current visualizations (notably node-link diagrams for large KGs). They propose visualization directions to improve usability, including knowledge cards, timelines, organic-discovery interfaces, and KG-based explainability, aiming to bridge technical and social gaps in KG adoption. The study highlights the need for domain-specific visualizations and interactive, interpretable interfaces that support both open-ended exploration and precise queries. Overall, the work offers actionable design directions for KG visualization research and practice to enhance adoption and insight generation across domains.

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.

Figures (3)

• Figure 1: An illustrative example of a knowledge graph (KG). In a KG, different types of entities (nodes) can have different types of relationships (edges) defined between them. We further discuss KGs in Section \ref{['sec:background']}.
• Figure 2: Three personas we identified for the users of knowledge graphs from our interviews, described in Section \ref{['sec:methodology']}. From the left, a user can be a KG Builder (e.g., database administrator), an Analyst (e.g., data scientist), or a Consumer (e.g., stakeholder). All three types of KG users have distinct roles, tasks, needs, and expertise -- however, it is possible a user can belong to more than one persona. For example, a user that creates their own KG of companies ("KG Builder") to predict which to invest into ("KG Analyst"). We further describe these personas in Section \ref{['sec:kg-roles']}.
• Figure 3: Left: an example knowledge card template with node and edge information that may be relevant to a KG end user. Right: an example knowledge card of a cybersecurity vulnerability that we iterated on with one of our participants to understand what might be useful to a cyber analyst. We describe knowledge cards in Section \ref{['sec:findings-knowledge-cards']}.