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A cross-linguistic entity alignment method based on graph convolutional neural network and graph attention network

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Abstract

Cross-language entity alignment forms an important component of building a Knowledge Graph. The task of cross-lingual entity alignment is to match entities in a source language with their counterparts in target languages. In practice, there is an imbalance of attribute information in corresponding entities at the same level, and the problem of neighboring point weight assignment is not considered, which not only loses the association information between entities but also limits the utilization of entity attributes in the alignment process, making this task challenging. In this paper, we propose a cross-lingual entity alignment method based on Graph convolutinal neural network and Graph attention network. Specifically, it can capture more spatial information by assigning respective weights to the neighbors of different nodes through multi-level learning of entity structure, attributes, and attention. In addition, the weights of neighboring node features depend entirely on the node features, which gets rid of the dependence on the graph. The experiments show that our models outperform state-of-the-art methods at a fraction of the cost.

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Data availibility

The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.

Code Availability

Original model codes are available in GitHub (https://github.com/1049451037/GCN-Align).Initial datasets are from [JAPE](https://github.com/nju-websoft/JAPE.)

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Acknowledgements

The research was supported by National Natural Science Foundation of China(No:61976027); Liaoning Provincial Education Department 2021 Scientific Research Project (LJKZ1028); Bohai University 2021 Graduate Education Teaching Reform Project (YJG20210022); Bohai University 2021 National Security Research Institute Project (XK202134-31); Bohai University 2022 Teaching Reform Project (2-7-47).

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  1. College of Information Science and Technology, Bohai University, Jinzhou, 121000, Liaoning, People’s Republic of China

    Zhen Zhao & Shuo Lin

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  1. Zhen Zhao
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Correspondence to Zhen Zhao.

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Zhao, Z., Lin, S. A cross-linguistic entity alignment method based on graph convolutional neural network and graph attention network. Computing 105, 2293–2310 (2023). https://doi.org/10.1007/s00607-023-01178-6

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