UKON-Buchmueller-MC1
Raphael Buchmüller - University of Konstanz, Konstanz, Germany
Daniel Fürst - University of Konstanz, Konstanz, Germany
Alexander Frings - University of Konstanz, Konstanz, Germany
Udo Schlegel - University of Konstanz, Konstanz, Germany
Daniel Keim - University of Konstanz, Konstanz, Germany
Room: Bayshore II
2024-10-13T12:30:00ZGMT-0600Change your timezone on the schedule page
2024-10-13T12:30:00Z
Full Video
Abstract
In this work, we present a visual analytics approach designed to address the 2024 VAST Challenge Mini-Challenge 1, which focuses on detecting bias in a knowledge graph. Our solution utilizes pixel-based visualizations to explore patterns within the knowledge graph, CatchNet, which is employed to identify potential illegal fishing activities. CatchNet is constructed by FishEye analysts who aggregate open-source data, including news articles and public reports. They have recently begun incorporating knowledge extracted from these sources using advanced language models. Our method combines pixel-based visualizations with ordering techniques and sentiment analysis to uncover hidden patterns in both the news articles and the knowledge graph. Notably, our analysis reveals that news articles covering critiques and convictions of companies are subject to elevated levels of bias.