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8 November 2024 Relational reasoning based on deep reinforcement learning
Shijie Gao, Cong Hu, Jiangbo Yin, Yi Shen
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Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 1341615 (2024) https://doi.org/10.1117/12.3049573
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China
Abstract
Large knowledge graphs always have the problem of missing relationships or entities in triples, and missing this information can lead to inference failure. To solve this problem, this paper proposes a knowledge graph inference method based on deep reinforcement learning. This method uses the idea of combining reinforcement learning with meta learning contrastive learning to complete the triplet information of the knowledge graph, and optimizes the reward function to improve the model performance. Comparative experiments were conducted on the NELL-995 and FB15K-237 datasets, and the experimental results showed that the proposed model outperformed most previous models.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Shijie Gao, Cong Hu, Jiangbo Yin, and Yi Shen "Relational reasoning based on deep reinforcement learning", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 1341615 (8 November 2024); https://doi.org/10.1117/12.3049573
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KEYWORDS
Machine learning
Deep learning
Performance modeling
Mathematical optimization
Reflection
Signal processing
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