Abstract
Multilingual knowledge graphs constructed by cross-lingual knowledge alignment have attracted increasing attentions in knowledge-driven cross-lingual research fields. Although many existing knowledge alignment methods such as MTransE based on linear transformations perform well on cross-lingual knowledge alignment, we note that neural networks with stronger nonlinear capacity of capturing alignment features. This paper proposes a knowledge alignment neural network named KANN for multilingual knowledge graphs. KANN combines a monolingual neural network for encoding the knowledge graph of each language into a separated embedding space, and a alignment neural network for providing transitions between cross-lingual embedding spaces. We empirically evaluate our KANN model on cross-lingual entity alignment task. Experimental results show that our method achieves significant and consistent performance, and outperforms the current state-of-the-art models.
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Acknowledgements
This work is supported by National Key R&D Program No.2017YFB0803003, and the National Natural Science Foundation of China (No.61202226), We thank all anonymous reviewers for their constructive comments.
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Zhu, Q., Zhou, X., Wu, Y., Liu, P., Guo, L. (2020). Multilingual Knowledge Graph Embeddings with Neural Networks. In: He, J., et al. Data Science. ICDS 2019. Communications in Computer and Information Science, vol 1179. Springer, Singapore. https://doi.org/10.1007/978-981-15-2810-1_15
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