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Harry X. Li

dblp:322/4810 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
0000-0002-2288-6039ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%
Databases, data mining, and information retrieval
1 paper
Knowledge graphs · 100%
Human-computer interaction and pervasive computing
1 paper
User interface design and tools · 100%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › graph visualization
knowledge graph visualization
0.812024
Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities · IEEE Trans. Vis. Comput. Graph. 2024
Visualization and visual analytics
visual analytics
0.812024
Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities · IEEE Trans. Vis. Comput. Graph. 2024

Methods — techniques the papers use, named apart from their topics

semi-structured interviews · 2.3persona analysis · 2.3
YearPublicationVenuePosition
2024 Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities
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.
Harry X. Li, Gabriel Appleby, Camelia D. Brumar, Remco Chang, Ashley Suh 0001
IEEE Trans. Vis. Comput. Graph.1