25 citations · 38 across the 6 of their papers we have counts for
6 papers
Vision, Deduction and Alignment: An Empirical Study on Multi-modal Knowledge Graph Alignment
Yangning Li, Jiaoyan Chen, Yinghui Li +3
Entity alignment (EA) for knowledge graphs (KGs) plays a critical role in knowledge engineering. Existing EA methods mostly focus on utilizing the graph structures and entity attri…
A Creative Industry Image Generation Dataset Based on Captions
Xiang Yuejia, Lv Chuanhao, Liu Qingdazhu +3
Most image generation methods are difficult to precisely control the properties of the generated images, such as structure, scale, shape, etc., which limits its large-scale applica…
X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning
Baoyu Jing, Shengyu Feng, Yuejia Xiang +3
Graphs are powerful representations for relations among objects, which have attracted plenty of attention. A fundamental challenge for graph learning is how to train an effective G…
Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding
Zhiyuan Qi, Ziheng Zhang, Jiaoyan Chen +4
Knowledge Graph (KG) alignment is to discover the mappings (i.e., equivalent entities, relations, and others) between two KGs. The existing methods can be divided into the embeddin…
OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding
Yuejia Xiang, Ziheng Zhang, Jiaoyan Chen +3
Semantic embedding has been widely investigated for aligning knowledge graph (KG) entities. Current methods have explored and utilized the graph structure, the entity names and att…
An Industry Evaluation of Embedding-based Entity Alignment
Ziheng Zhang, Jiaoyan Chen, Xi Chen +4
Embedding-based entity alignment has been widely investigated in recent years, but most proposed methods still rely on an ideal supervised learning setting with a large number of u…