Design of a multi-level domain knowledge graph reasoning framework based on temporal data

Yuting Zhou; Yonghong Chen; Han Li; Gang Sun; Wenhao Liu; Hao Li; Yirui Wu; Qian Huang

doi:10.1117/12.3008408

14 November 2023 Design of a multi-level domain knowledge graph reasoning framework based on temporal data

Yuting Zhou, Yonghong Chen, Han Li, Gang Sun, Wenhao Liu, Hao Li, Yirui Wu, Qian Huang

Proceedings Volume 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023); 129341Q (2023) https://doi.org/10.1117/12.3008408
Event: 2023 3rd International Conference on Computer Graphics, Image and Virtualization (ICCGIV 2023), 2023, Nanjing, China

Abstract

As a hot research direction in current academic studies, knowledge graph reasoning is aimed at solving the many challenges and pain points of knowledge graphs. This paper centers around temporal data prediction and presents a multi-level framework that leverages causal knowledge graphs. Our framework seamlessly integrates causal knowledge graphs with temporal data to enhance prediction accuracy. The framework is composed of two key components: causal knowledge graph construction and multi-level gated graph neural network prediction. By representing facts and relationships within the domain using causal knowledge graphs, the framework enhances the capability of the temporal data prediction model. The proposed framework design can provide better knowledge understanding for researchers in the field and achieve accurate prediction of temporal data.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuting Zhou, Yonghong Chen, Han Li, Gang Sun, Wenhao Liu, Hao Li, Yirui Wu, and Qian Huang "Design of a multi-level domain knowledge graph reasoning framework based on temporal data", Proc. SPIE 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023), 129341Q (14 November 2023); https://doi.org/10.1117/12.3008408

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