Abstract
Cross-lingual entity alignment is the task of finding the same semantic entities from different language knowledge graphs. In this paper, we propose a simple and novel unsupervised method for cross-language entity alignment. We utilize the deep learning multi-language encoder combined with a machine translator to encode knowledge graph text, which reduces the reliance on label data. Unlike traditional methods that only emphasize global or local alignment, our method simultaneously considers both alignment strategies. We first view the alignment task as a bipartite matching problem and then adopt the re-exchanging idea to accomplish alignment. Compared with the traditional bipartite matching algorithm that only gives one optimal solution, our algorithm generates ranked matching results which enabled many potentials downstream tasks. Additionally, our method can adapt two different types of optimization (minimal and maximal) in the bipartite matching process, which provides more flexibility. Our evaluation shows, we each scored 0.966, 0.990, and 0.996 Hits@1 rates on the \(\textrm{DBP15K}\) dataset in Chinese, Japanese, and French to English alignment tasks. We outperformed the state-of-the-art method in unsupervised and semi-supervised categories. Compared with the state-of-the-art supervised method, our method outperforms 2.6% and 0.4% in Ja-En and Fr-En alignment tasks while marginally lower by 0.2% in the Zh-En alignment task.
C. Jiang and Y. Qian—Contribute equally to this work.
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Notes
- 1.
Our source code is available in https://github.com/chuanyus/UDCEA.
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Acknowledgement
This work is supported by Hainan Province Science and Technology Special Fund (No. ZDKJ2021042) and A*STAR under its Artificial Intelligence in Medicine Transformation (AIMx) Program (Award H20C6a0032) National Natural Science Foundation of China (No.62362023).
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Jiang, C., Qian, Y., Chen, L., Gu, Y., Xie, X. (2023). Unsupervised Deep Cross-Language Entity Alignment. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14172. Springer, Cham. https://doi.org/10.1007/978-3-031-43421-1_1
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