Scientific research's duty and goal is to comprehend and explore the world, as well as to modify it based on experience and knowledge. Knowledge embedding and knowledge discovery are two significant methods of integrating knowledge and data. Through knowledge embedding, the barriers between knowledge and data can be broken, and machine learning models with physical common sense can be formed. Meanwhile, humans' understanding of the world is always limited, and knowledge discovery takes advantage of machine learning to extract new knowledge from observations. Not only may knowledge discovery help researchers better grasp the nature of physics, but it can also help them conduct knowledge embedding research. A closed loop of knowledge generation and usage are formed by combining knowledge embedding with knowledge discovery, which can improve the robustness and accuracy of the model and uncover unknown scientific principles. This study not only summarizes and analyzes the existing literature, but also proposes research gaps and future opportunities.

该研究总结和分析了现有文献，确定了研究空白和未来机遇。知识嵌入和知识发现是整合知识和数据的两种重要方法，结合知识嵌入和知识发现形成一个闭环的知识生成和使用，可以提高模型的鲁棒性和准确性，并发现之前未知的科学原理。

机器学习中知识与数据的融合