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.
Authors
- Ailing Guo (ORCID: https://orcid.org/0000-0003-0529-141X)
- Xiao‐Bao Shu (ORCID: https://orcid.org/0000-0002-8256-5130)
- Yi Shan (ORCID: https://orcid.org/0009-0007-7549-8738)
- Meng Guo (ORCID: https://orcid.org/0009-0003-3506-0636)
- Qianhui Li (ORCID: https://orcid.org/0009-0000-7924-9842)
- Siming Chen (ORCID: https://orcid.org/0000-0002-2690-3588)
- Tian Qiu
Institutions
- Fudan University (CN)
Publication Details
- 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