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.

翻译：人工智能（AI）的快速发展，特别是深度神经网络的进步，显著推动了视觉和文本处理等领域的发展。然而，构建具有类人推理能力和可解释性的AI系统仍面临重大挑战。神经符号范式融合了神经网络的深度学习能力与符号系统的推理功能，为实现更透明、可理解的AI系统提供了有前景的路径。在此范式中，知识图谱作为关键要素出现，通过相互关联的实体和关系（主要采用三元组结构：主体、谓词、客体）提供了结构化且动态的知识表示方法。本文探讨了基于知识图谱的神经符号集成最新进展，阐释了知识图谱如何通过三种关键类别支撑该集成：通过融入符号知识增强神经网络的推理能力与可解释性（符号增强神经）、通过神经网络方法完善符号系统的完整性与准确性（神经增强符号）、以及促进两者在混合神经符号集成中的协同应用。本文还指出了当前研究趋势，并为神经符号人工智能领域的未来研究方向提出了建议。