Knowledge representation learning method based on background information and adaptive weight measurement

Chuanwen Zhou; Heng Qian

doi:10.1117/12.3006594

26 September 2023 Knowledge representation learning method based on background information and adaptive weight measurement

Chuanwen Zhou, Heng Qian

Author Affiliations +

Proceedings Volume 12793, International Conference on Mechatronics and Intelligent Control (ICMIC 2023); 127930X (2023) https://doi.org/10.1117/12.3006594
Event: International Conference on Mechatronics and Intelligent Control (ICMIC2023), 2023, Wuhan, China

Abstract

Currently, the knowledge representation learning methods are based on knowledge background information and integrated with the calculation related to correlation strength, type matching degree, and multi-part path. However, besides excellent learning effects, their loss functions are simple so that more complex knowledge graphs cannot be represented well. In order to solve these problems, a more flexible knowledge representation learning model is proposed in the paper and the adaptive measure functions are imported to optimize the loss functions in the knowledge representation learning method. The experiment was performed on several baseline data sets and optimal results are well obtained.

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

Citation Download Citation

Chuanwen Zhou and Heng Qian "Knowledge representation learning method based on background information and adaptive weight measurement", Proc. SPIE 12793, International Conference on Mechatronics and Intelligent Control (ICMIC 2023), 127930X (26 September 2023); https://doi.org/10.1117/12.3006594

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