Machine learning has been heavily researched and widely used in many disciplines. However, achieving high accuracy requires a large amount of data that is sometimes difficult, expensive, or impractical to obtain. Integrating human knowledge into machine learning can significantly reduce data requirement, increase reliability and robustness of machine learning, and build explainable machine learning systems. This allows leveraging the vast amount of human knowledge and capability of machine learning to achieve functions and performance not available before and will facilitate the interaction between human beings and machine learning systems, making machine learning decisions understandable to humans. This paper gives an overview of the knowledge and its representations that can be integrated into machine learning and the methodology. We cover the fundamentals, current status, and recent progress of the methods, with a focus on popular and new topics. The perspectives on future directions are also discussed.

机器学习在许多学科中得到了大量研究和广泛应用。然而，实现高精度需要大量数据，而获取这些数据有时很困难、昂贵或不切实际。将人类知识融入机器学习可以显着减少数据需求，提高机器学习的可靠性和鲁棒性，并构建可解释的机器学习系统。这使得能够利用人类大量的知识和机器学习的能力来实现以前无法实现的功能和性能，并将促进人类与机器学习系统之间的交互，使机器学习决策能够为人类所理解。本文概述了可以集成到机器学习和方法中的知识及其表示形式。我们涵盖了这些方法的基础知识、现状和最新进展，重点关注热门和新主题。还讨论了对未来方向的看法。