Integration of knowledge and data in machine learning
It addresses the problem of enhancing machine learning models by combining knowledge and data for researchers, but it is incremental as it summarizes existing literature without presenting new results.
This paper reviews methods for integrating knowledge and data in machine learning, focusing on knowledge embedding to create models with physical common sense and knowledge discovery to extract new insights from observations, aiming to improve model robustness and accuracy and uncover scientific principles.
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.