Learning multilevel representations for knowledge graph embedding using graph neural networks

Dongqing Zhang

doi:10.1117/12.3035085

19 July 2024 Learning multilevel representations for knowledge graph embedding using graph neural networks

Dongqing Zhang

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132131N (2024) https://doi.org/10.1117/12.3035085
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

The ability to visualize the semantic connections between relationships and entities is a powerful feature of knowledge graphs. Unfortunately, it is typically challenging to extract the multi-level information of these relationships and entities from the knowledge graph. We have developed a method that can be used to learn such connections using a graph neural network. Our model effectively captures the hierarchical information in the knowledge graph by mapping entities and relationships into a multi-level graph neural network. Specifically, we design multiple graph convolutional layers and attention mechanisms to achieve multi-level embedded representations of entities and relationships.

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

Citation Download Citation

Dongqing Zhang "Learning multilevel representations for knowledge graph embedding using graph neural networks", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132131N (19 July 2024); https://doi.org/10.1117/12.3035085

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