Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment

1 code implementation • 2 May 2023 • Zhishuo Zhang, Chengxiang Tan, Haihang Wang, Xueyan Zhao, Min Yang

Entity alignment(EA) is a crucial task for integrating cross-lingual and cross-domain knowledge graphs(KGs), which aims to discover entities referring to the same real-world object from different KGs.

Entity Alignment Knowledge Graphs +1

OTIEA:Ontology-enhanced Triple Intrinsic-Correlation for Cross-lingual Entity Alignment

1 code implementation • 2 May 2023 • Zhishuo Zhang, Chengxiang Tan, Xueyan Zhao, Min Yang, Chaoqun Jiang

In addition, external resources are usually unavailable in some scenarios especially cross-lingual and cross-domain applications, which reflects the little scalability of these methods.

Entity Alignment Knowledge Graphs

Weak Identification in Discrete Choice Models

no code implementations • 13 Nov 2020 • David T. Frazier, Eric Renault, Lina Zhang, Xueyan Zhao

We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models.

Discrete Choice Models

Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects

no code implementations • 6 Sep 2020 • Lina Zhang, David T. Frazier, D. S. Poskitt, Xueyan Zhao

This paper examines the identification power of instrumental variables (IVs) for average treatment effect (ATE) in partially identified models.