基于强化学习的知识图谱推理研究综述.

Knowledge reasoning is a fundamental task in knowledge graph completion. It is a popular topic in the academie community. Integrating reinforcement learning and knowledge reasoning is a viable solution to improve the inference effectiveness and interpretability of models. Taking the problem of knowledge graph research as a path or sequence decision problem in the knowledge reasoning method based on reinforcement learning can make better use of semantic information, such as entities and relationships, to improve reasoning effect and interpretability. This paper provided a descriptive overview of the basic concepts of knowledge graph and knowledge reasoning, and described the research progress in recent years. The paper analysed and compared the related research on knowledge reasoning based on reinforcement learning from two perspectives; single-layer and double-layer reinforcement learning knowledge reasoning. Furthermore, the paper explored the application of knowledge reasoning in various domains, including knowledge question answering, intelligent recommendation systems, healthcare, and transportation. Lastly, the paper discussed future research tendencies for reinforcement learning-based knowledge reasoning and offered insights into potential avenues for exploration and development. [ABSTRACT FROM AUTHOR]