Work

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

Year: 2023

Type: article

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.

Source: IEEE Transactions on Visualization and Computer Graphics

DOI: 10.1109/tvcg.2023.3326904

Authors: Harry Li, Gabriel Appleby, Camelia Daniela Brumar, Remco Chang, Ashley Suh

Institutions: MIT Lincoln Laboratory, Tufts University

Language: English

Topic: Data Visualization and Analytics

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Awards: Collaborative Research: Accelerating the Discovery of Electronic Materials through Human-Computer Active Search, HDR Institute: Institute for Data Driven Dynamical Design, Collaborative Research: Converging Genomics, Phenomics, and Environments Using Interpretable Machine Learning Models, CAREER: Analyzing Interactions in Visual Analytics for User and Data Modeling, NRT-HDR Data Driven Decision Making to Address Complex Resource Problems