FishEye Watcher: A Visual Analytics System for Knowledge Graph Bias Detection - IEEE VAST Challenge 2024 MC1 Honorable Mention for Effective Use of Coordinated Views to Interrogate Bias

In this paper we present an interactive visualization system for solving IEEE VAST Challenge 2024 Mini-Challenge 1. Our system enables interactive exploration and mining of the knowledge graph, assists in identifying suspicious bias and provides corresponding evidence from multiple perspectives. For the convenience of user exploration, our system supports recording the exploration process and preservation of evidence. The illustrative case proves the effectiveness of our system.

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Published
2024-10-13
DOI
https://doi.org/10.1109/vastchallenge64683.2024.00011
Primary Topic
Occupational Health and Safety Research
Type
article
Field-Weighted Citation Impact
0.00
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article

FishEye Watcher: A Visual Analytics System for Knowledge Graph Bias Detection - IEEE VAST Challenge 2024 MC1 Honorable Mention for Effective Use of Coordinated Views to Interrogate Bias

Ailing Guo, Xiao‐Bao Shu, Yi Shan, Meng Guo et al.
Occupational Health and Safety Research
article

FishEye Watcher: A Visual Analytics System for Knowledge Graph Bias Detection - IEEE VAST Challenge 2024 MC1 Honorable Mention for Effective Use of Coordinated Views to Interrogate Bias

Ailing Guo, Xiao‐Bao Shu, Yi Shan, Meng Guo, Qianhui Li, Siming Chen, Tian Qiu
article en

Abstract

In this paper we present an interactive visualization system for solving IEEE VAST Challenge 2024 Mini-Challenge 1. Our system enables interactive exploration and mining of the knowledge graph, assists in identifying suspicious bias and provides corresponding evidence from multiple perspectives. For the convenience of user exploration, our system supports recording the exploration process and preservation of evidence. The illustrative case proves the effectiveness of our system.

Fudan University (CN)
Openalex Percentile: Top 69%
Occupational Health and Safety Research
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