Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs

By Yuting Wu, Xiao Liu, Yansong Feng, Zheng Wang, Rui Yan, Dongyan Zhao. International Joint Conference on Artificial Intelligence, 2019

398 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/7338202391972f1441edbdeaf5aaf266d15777a1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Wei Hu
2. Zequn Sun
3. Wenbing Huang
4. Muhao Chen
5. Chengming Wang
6. Yuzhong Qu
7. Wei Zhang
8. Jian Dai

In Management Science and Operations Research

1. Wei Hu
2. Zequn Sun
3. Wenbing Huang
4. Junzhou Huang
5. Yuzhong Qu
6. Muhao Chen
7. Chengming Wang
8. Wei Zhang

Citations in the corpus

• Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation (2019, methodology): “2019), RDGCN (Wu et al. 2019) and AVR-GCN (Ye et al.”
• Graph Representation Learning via Graphical Mutual Information Maximization (2020, methodology): “Recent methods towards this goal, commonly categorized as Graph Neural Networks (GNNs), have made remarkable advancements in a great many learning tasks, such as node classification [35, 42, 50], link prediction [26, 27, 49], graph alignment [12, 19, 43], molecular generation [4, 5, 46], to name a f”
• A benchmarking study of embedding-based entity alignment for knowledge graphs (2020, neutral): “Attribute embedding is used by several approaches [9, 29, 80, 84, 89, 92, 94, 96, 100] to enhance the similarity measure of entities.”