8 citations · 10 across the 3 of their papers we have counts for
3 papers
i-Align: an interpretable knowledge graph alignment model
Bayu Distiawan Trisedya, Flora D Salim, Jeffrey Chan +3
Knowledge graphs (KGs) are becoming essential resources for many downstream applications. However, their incompleteness may limit their potential. Thus, continuous curation is need…
AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models
Rui Zhang, Yixin Su, Bayu Distiawan Trisedya +4
The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity. Many machine learning-bas…
TransAlign: Fully Automatic and Effective Entity Alignment for Knowledge Graphs
Rui Zhang, Xiaoyan Zhao, Bayu Distiawan Trisedya +3
The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity. Many machine learning-bas…