274 citations · 324 across the 3 of their papers we have counts for
4 papers
On Large-scale Evaluation of Embedding Models for Knowledge Graph Completion
Nasim Shirvani-Mahdavi, Farahnaz Akrami, Chengkai Li
Knowledge graph embedding (KGE) models are extensively studied for knowledge graph completion, yet their evaluation remains constrained by unrealistic benchmarks. Standard evaluati…
Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study
Farahnaz Akrami, Mohammed Samiul Saeef, Qingheng Zhang +2
In the active research area of employing embedding models for knowledge graph completion, particularly for the task of link prediction, most prior studies used two benchmark datase…
A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs
Zequn Sun, Qingheng Zhang, Wei Hu +4
Entity alignment seeks to find entities in different knowledge graphs (KGs) that refer to the same real-world object. Recent advancement in KG embedding impels the advent of embedd…
Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
Zequn Sun, Wei Hu, Chengkai Li
Entity alignment is the task of finding entities in two knowledge bases (KBs) that represent the same real-world object. When facing KBs in different natural languages, conventiona…