Data and domain knowledge dual‐driven artificial intelligence: Survey, applications, and challenges
Jing Nie, Jiachen Jiang, Yang Li, Huting Wang, Sezai Erċışlı, Linze Lv
Abstract
Abstract At present, the mainstream mode of machine learning algorithms is the data‐driven method, which mainly relies on the self‐learning ability of deep neural networks and continuously evolving models in data‐driven training. However, the pure data‐driven method has some critical problems, such as high data collection cost, poor interpretability and easy to be be disturbed by noise. Although the knowledge‐driven method has high stability, it lacks self‐learning and evolution ability in the face of comprehensive and complex problems. In recent years, the convergence of data and domain knowledge has combined the advantages of both learning paradigms. One typical way is to embed domain knowledge into the data‐driven model to improve the interpretability of the model, and then use the self‐learning ability of the data‐driven model to explore knowledge, and continuously iterate the domain knowledge to form a closed loop. The data‐knowledge dual‐driven methods have brought transformative innovations in machine learning. This review first introduced the advantages and necessity of the data‐knowledge dual‐driven model in the field of artificial intelligence. Then, the applications of the data‐knowledge dual‐driven model in the smart marine field were introduced. Finally, the challenges and trends of the data‐knowledge dual‐driven artificial intelligence are anticipated.