RuMER-RL: A hybrid framework for sparse knowledge graph explainable reasoning

Zefan Zeng; Qing Cheng; Yuehang Si; Zhong Liu

doi:10.1016/j.ins.2024.121144

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RuMER-RL: A hybrid framework for sparse knowledge graph explainable reasoning

Zefan Zeng Qing Cheng Yuehang Si Zhong Liu

Year: 2024 Journal: Information Sciences Vol: 680 Pages: 121144-121144 Publisher: Elsevier BV

DOI: 10.1016/j.ins.2024.121144

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Keywords:

Interpretability Computer science Embedding Reinforcement learning Markov decision process Artificial intelligence Graph Representation (politics) Machine learning Automated planning and scheduling Theoretical computer science Markov process Mathematics

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Advanced Graph Neural Networks

Physical Sciences → Computer Science → Artificial Intelligence

Bayesian Modeling and Causal Inference

Physical Sciences → Computer Science → Artificial Intelligence

Recommender Systems and Techniques

Physical Sciences → Computer Science → Information Systems

RuMER-RL: A hybrid framework for sparse knowledge graph explainable reasoning

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