IKGA: An Interactive Visualization Tool for Knowledge Graph Alignment

Weixin Zeng, Shiqi Zhang, Huang Peng, Zhen Tan, Weidong Xiao, Xiang Zhao

Abstract: Knowledge Graph Alignment (KGA) identifies elements in different knowledge graphs (KGs) that refer to the same real-world object. It is a key step towards improving the coverage of KGs, which in turn can better facilitate downstream tasks. However, there is currently no interactive tool to support KGA research, particularly for visualizing alignment results, hence limiting the understanding of the procedure and also the development of more advanced solutions. To fill in this gap, in this paper, we introduce IKGA, an interactive visualization tool for KGA, which visualizes the alignment process by integrating various algorithms of representation learning and alignment inference-two key steps in KGA. The system allows users to observe KGA results, compare existing algorithms, and apply to domain-specific KGs at hand.

External IDs:dblp:conf/icde/ZengZPTXZ25