Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs

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.

Authors

• Yuting Wu School of Software, Beijing Jiaotong University, Beijing, China
• Xiao Liu Microsoft Research
• Yansong Feng
• Zheng Wang National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, China Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, China
• Rui Yan
• Dongyan Zhao Wangxuan Institute of Computer Technology, Peking University National Key Laboratory of General Artificial Intelligence

Keywords

• Natural Language Processing: Knowledge Extraction
• Natural Language Processing: Natural Language Processing
• Natural Language Processing: NLP Applications and Tools