The graph attention network in relational reasoning based on knowledge graph

Yan Yan; Mengtian Zhang; Chunsheng Xu

doi:10.1117/12.3021487

19 February 2024 The graph attention network in relational reasoning based on knowledge graph

Yan Yan, Mengtian Zhang, Chunsheng Xu

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 130632V (2024) https://doi.org/10.1117/12.3021487
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

The knowledge graph in the knowledge graph is sparse due to missing relationships and incomplete triples, which promotes research on knowledge graph completion (relationship prediction). However, the purpose of knowledge graph reasoning technology is to infer unknown knowledge or identify wrong knowledge based on the knowledge learned in the knowledge graph to improve the knowledge graph. The graph attention network (GAT) can improve the input constraints of RNN and convolution and introduce a self-attention architecture to perform classification of graph-structured data. However, the model still has two limitations: applying the model in the knowledge graph will ignore the relationship (edge) features; Currently popular models can only use the k-th hop output for multi-hop embedding learning, resulting in the loss of a lot of early embedding information (such as one hop) during the graph attention process. This paper proposes a new method to improve the quality of the model by embedding relational features.

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

Citation Download Citation

Yan Yan, Mengtian Zhang, and Chunsheng Xu "The graph attention network in relational reasoning based on knowledge graph", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 130632V (19 February 2024); https://doi.org/10.1117/12.3021487

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