The rapid advancement in artificial intelligence (AI), particularly through
deep neural networks, has catalyzed significant progress in fields such as
vision and text processing. Nonetheless, the pursuit of AI systems that exhibit
human-like reasoning and interpretability continues to pose a substantial
challenge. The Neural-Symbolic paradigm, which integrates the deep learning
prowess of neural networks with the reasoning capabilities of symbolic systems,
presents a promising pathway toward developing more transparent and
comprehensible AI systems. Within this paradigm, the Knowledge Graph (KG)
emerges as a crucial element, offering a structured and dynamic method for
representing knowledge through interconnected entities and relationships,
predominantly utilizing the triple (subject, predicate, object). This paper
explores recent advancements in neural-symbolic integration based on KG,
elucidating how KG underpins this integration across three key categories:
enhancing the reasoning and interpretability of neural networks through the
incorporation of symbolic knowledge (Symbol for Neural), refining the
completeness and accuracy of symbolic systems via neural network methodologies
(Neural for Symbol), and facilitating their combined application in Hybrid
Neural-Symbolic Integration. It highlights current trends and proposes
directions for future research in the domain of Neural-Symbolic AI.

人工智能的快速发展，特别是通过深度神经网络，已经在视觉和文本处理等领域取得了显著进展。但是，实现人类类似的推理和可解释的人工智能系统仍然面临重大挑战。神经符号学范式将神经网络的深度学习能力与符号系统的推理能力结合起来，提供了开发更透明和可理解的人工智能系统的有希望途径。本文探讨了基于知识图谱的神经符号一体化的最新进展，阐明了知识图谱在三个关键类别中支撑着这种一体化：通过引入符号知识来增强神经网络的推理和可解释性（Symbol for Neural），通过神经网络方法来提高符号系统的完整性和准确性（Neural for Symbol），以及促进两者在混合神经符号一体化中的应用。本文强调了当前的趋势，并提出了神经符号人工智能领域未来研究的方向。