ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment
- URL: http://arxiv.org/abs/2402.11000v2
- Date: Tue, 5 Mar 2024 13:57:28 GMT
- Title: ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment
- Authors: Yangyifei Luo, Zhuo Chen, Lingbing Guo, Qian Li, Wenxuan Zeng, Zhixin
Cai, Jianxin Li
- Abstract summary: We propose the Align-Subgraph Entity Alignment (ASGEA) framework to exploit logic rules from Align-Subgraphs.
We also design an interpretable Path-based Graph Neural Network, ASGNN, to effectively identify and integrate the logic rules across KGs.
Our experimental results demonstrate the superior performance of ASGEA over the existing embedding-based methods in both EA and Multi-Modal EA (MMEA) tasks.
- Score: 15.527373618633847
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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