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Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning
[article]
2020
arXiv
pre-print
Path-based relational reasoning over knowledge graphs has become increasingly popular due to a variety of downstream applications such as question answering in dialogue systems, fact prediction, and recommender systems. In recent years, reinforcement learning (RL) has provided solutions that are more interpretable and explainable than other deep learning models. However, these solutions still face several challenges, including large action space for the RL agent and accurate representation of
arXiv:2003.06050v1
fatcat:jfw2bdpgrbe7lbxjgtthkg5ggi