Cross-language entity alignment based on graph convolution neural network and attribute information

Xiaozhan Hu; Yuan Sun

doi:10.1117/12.3031901

6 June 2024 Cross-language entity alignment based on graph convolution neural network and attribute information

Xiaozhan Hu, Yuan Sun

Proceedings Volume 13175, International Conference on Computer Network Security and Software Engineering (CNSSE 2024); 1317515 (2024) https://doi.org/10.1117/12.3031901
Event: 4th International Conference on Computer Network Security and Software Engineering (CNSSE 2024), 2024, Sanya, China

Abstract

Knowledge graphs are widely used in the field of natural language processing applications. In order to study how to use the structural and attribute information of entities for cross language entity alignment, we have successively borrowed the high-speed gate mechanism of the HGCN model and the relationship aware neighborhood matching model of the RNM model. Firstly, using Graph Convolutional Neural Network (GCN) for knowledge graph embedding learning, and then introducing the method of attribute information and highway gates mechanism to jointly embed the structure and attributes for learning. In entity alignment, relationship aware neighborhood matching is used to improve alignment performance. Therefore, this article proposes a research method for entity alignment based on graph convolutional neural networks and attribute information. Experiments were conducted on the publicly available dataset DBP15k, and from the results, it can be seen that Hits@1 The indicators reached 85.24%, 87.26%, and 94.76% respectively, achieving better experimental results.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaozhan Hu and Yuan Sun "Cross-language entity alignment based on graph convolution neural network and attribute information", Proc. SPIE 13175, International Conference on Computer Network Security and Software Engineering (CNSSE 2024), 1317515 (6 June 2024); https://doi.org/10.1117/12.3031901

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KEYWORDS

Alignment modeling

Convolution

Convolutional neural networks

Distributed interactive simulations

Matrices

Neural networks

Data modeling

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