research-article

Dual Gated Graph Attention Networks with Dynamic Iterative Training for Cross-Lingual Entity Alignment

Authors:

Jimmy Xiangji HuangAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 40, Issue 3

Article No.: 44, Pages 1 - 30

https://doi.org/10.1145/3471165

Published: 17 November 2021 Publication History

Abstract

Cross-lingual entity alignment has attracted considerable attention in recent years. Past studies using conventional approaches to match entities share the common problem of missing important structural information beyond entities in the modeling process. This allows graph neural network models to step in. Most existing graph neural network approaches model individual knowledge graphs (KGs) separately with a small amount of pre-aligned entities served as anchors to connect different KG embedding spaces. However, this characteristic can cause several major problems, including performance restraint due to the insufficiency of available seed alignments and ignorance of pre-aligned links that are useful in contextual information in-between nodes. In this article, we propose DuGa-DIT, a dual gated graph attention network with dynamic iterative training, to address these problems in a unified model. The DuGa-DIT model captures neighborhood and cross-KG alignment features by using intra-KG attention and cross-KG attention layers. With the dynamic iterative process, we can dynamically update the cross-KG attention score matrices, which enables our model to capture more cross-KG information. We conduct extensive experiments on two benchmark datasets and a case study in cross-lingual personalized search. Our experimental results demonstrate that DuGa-DIT outperforms state-of-the-art methods.

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Index Terms

Dual Gated Graph Attention Networks with Dynamic Iterative Training for Cross-Lingual Entity Alignment
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
    2. Natural language processing
2. Information systems
  1. Data management systems
    1. Information integration
      1. Entity resolution
  2. Information retrieval

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 40, Issue 3

July 2022

650 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3498357

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 17 November 2021

Accepted: 01 June 2021

Revised: 01 April 2021

Received: 01 November 2020

Published in TOIS Volume 40, Issue 3

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