1. ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment
- Author
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Luo, Yangyifei, Chen, Zhuo, Guo, Lingbing, Li, Qian, Zeng, Wenxuan, Cai, Zhixin, and Li, Jianxin
- Subjects
Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Entity alignment (EA) aims to identify entities across different knowledge graphs that represent the same real-world objects. Recent embedding-based EA methods have achieved state-of-the-art performance in EA yet faced interpretability challenges as they purely rely on the embedding distance and neglect the logic rules behind a pair of aligned entities. In this paper, we propose the Align-Subgraph Entity Alignment (ASGEA) framework to exploit logic rules from Align-Subgraphs. ASGEA uses anchor links as bridges to construct Align-Subgraphs and spreads along the paths across KGs, which distinguishes it from the embedding-based methods. Furthermore, we design an interpretable Path-based Graph Neural Network, ASGNN, to effectively identify and integrate the logic rules across KGs. We also introduce a node-level multi-modal attention mechanism coupled with multi-modal enriched anchors to augment the Align-Subgraph. Our experimental results demonstrate the superior performance of ASGEA over the existing embedding-based methods in both EA and Multi-Modal EA (MMEA) tasks., Comment: Ongoing work; 16 pages, 9 Tables, 8 Figures; Code: https://github.com/lyyf2002/ASGEA
- Published
- 2024