Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs

Abstract

Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods. Such approaches work by learning KG representations so that entity alignment can be performed by measuring the similarities between entity embeddings. While promising, prior works in the field often fail to properly capture complex relation information that commonly exists in multi-relational KGs, leaving much room for improvement. In this paper, we propose a novel Relation-aware Dual-Graph Convolutional Network (RDGCN) to incorporate relation information via attentive interactions between the knowledge graph and its dual relation counterpart, and further capture neighboring structures to learn better entity representations. Experiments on three real-world cross-lingual datasets show that our approach delivers better and more robust results over the state-of-the-art alignment methods by learning better KG representations.

Accepted by IJCAI 2019

References in corpus (7)

• Semi-Supervised Classification with Graph Convolutional Networks
• Inductive Representation Learning on Large Graphs
• Spectral Networks and Locally Connected Networks on Graphs
• Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
• Diffusion-Convolutional Neural Networks
• Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
• Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment

Cited by in corpus (9)

• Boosting the Speed of Entity Alignment 10*: Dual Attention Matching Network with Normalized Hard Sample Mining
• Informed Multi-context Entity Alignment
• Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding
• High-quality Task Division for Large-scale Entity Alignment
• Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment
• Mitigating Modality Bias in Multi-modal Entity Alignment from a Causal Perspective
• A Critical Assessment of State-of-the-Art in Entity Alignment
• Unsupervised Entity Alignment Based on Personalized Discriminative Rooted Tree
• Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey