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20212025
most citedMitigating LLM Hallucinations with Knowledge Graphs: A Case Study

2 citations · 6 across the 7 of their papers we have counts for

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cs.HC20252 cited

Mitigating LLM Hallucinations with Knowledge Graphs: A Case Study

Harry Li, Gabriel Appleby, Kenneth Alperin +2

High-stakes domains like cyber operations need responsible and trustworthy AI methods. While large language models (LLMs) are becoming increasingly popular in these domains, they s…

cs.HC20241 cited

DimBridge: Interactive Explanation of Visual Patterns in Dimensionality Reductions with Predicate Logic

Brian Montambault, Gabriel Appleby, Jen Rogers +3

Dimensionality reduction techniques are widely used for visualizing high-dimensional data. However, support for interpreting patterns of dimension reduction results in the context…

cs.HC2024

A Typology of Decision-Making Tasks for Visualization

Camelia D. Brumar, Sam Molnar, Gabriel Appleby +2

Despite decision-making being a vital goal of data visualization, little work has been done to differentiate decision-making tasks within the field. While visualization task taxono…

cs.HC20242 cited

A Preliminary Roadmap for LLMs as Assistants in Exploring, Analyzing, and Visualizing Knowledge Graphs

Harry Li, Gabriel Appleby, Ashley Suh

We present a mixed-methods study to explore how large language models (LLMs) can assist users in the visual exploration and analysis of knowledge graphs (KGs). We surveyed and inte…

cs.HC2023

RekomGNN: Visualizing, Contextualizing and Evaluating Graph Neural Networks Recommendations

Camelia D. Brumar, Gabriel Appleby, Jen Rogers +4

Content recommendation tasks increasingly use Graph Neural Networks, but it remains challenging for machine learning experts to assess the quality of their outputs. Visualization s…

cs.HC2023

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

Harry Li, Gabriel Appleby, Camelia Daniela Brumar +2

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. Thro…