From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment
This work addresses the need for efficient and interpretable entity alignment in knowledge graph integration, offering a novel solution that is not incremental but shifts the paradigm away from neural network-based methods.
The paper tackled the problem of cross-lingual entity alignment in knowledge graphs by transforming it into an assignment problem, resulting in an unsupervised method that outperforms supervised approaches on public datasets with high efficiency and interpretability.
Cross-lingual entity alignment (EA) aims to find the equivalent entities between crosslingual KGs, which is a crucial step for integrating KGs. Recently, many GNN-based EA methods are proposed and show decent performance improvements on several public datasets. Meanwhile, existing GNN-based EA methods inevitably inherit poor interpretability and low efficiency from neural networks. Motivated by the isomorphic assumption of GNNbased methods, we successfully transform the cross-lingual EA problem into the assignment problem. Based on this finding, we propose a frustratingly Simple but Effective Unsupervised entity alignment method (SEU) without neural networks. Extensive experiments show that our proposed unsupervised method even beats advanced supervised methods across all public datasets and has high efficiency, interpretability, and stability.