ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment
<|reference_start|>ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment: 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.<|reference_end|>
@article{luo2024asgea:,
    title={ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment},
    author={Yangyifei Luo, Zhuo Chen, Lingbing Guo, Qian Li, Wenxuan Zeng, Zhixin
  Cai, Jianxin Li},
    journal={arXiv preprint arXiv:2402.11000},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11000},
    primaryClass={cs.CL cs.AI}
}