论文标题
知识和数据在机器学习中的集成
Integration of knowledge and data in machine learning
论文作者
论文摘要
科学研究的任务是理解和探索世界，并根据经验和知识来改善世界。知识嵌入和知识发现是整合知识和数据的两种重要方法。通过知识嵌入，可以消除知识和数据之间的障碍，并且可以建立具有物理常识的机器学习模型。同时，人类对世界的理解始终是有限的，知识发现利用了机器学习从观察结果中提取新知识。知识发现不仅可以帮助研究人员更好地掌握物理学的性质，而且还可以支持他们进行知识嵌入研究。通过将嵌入知识与知识发现相结合，可以提高模型的鲁棒性和准确性，并发现以前未知的科学原理，从而形成了知识生成和用法的封闭循环。这项研究总结并分析了现有的文献，并确定了研究差距和未来机会。
Scientific research's mandate is to comprehend and explore the world, as well as to improve 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 eliminated, and machine learning models with physical common sense can be established. Meanwhile, humans' understanding of the world is always limited, and knowledge discovery takes advantage of machine learning to extract new knowledge from observations. Knowledge discovery can not only assist researchers to better grasp the nature of physics, but it can also support them in conducting 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 models and uncover previously unknown scientific principles. This study summarizes and analyzes extant literature, as well as identifies research gaps and future opportunities.
