  Advancements in Artificial Intelligence (AI) and deep neural networks have
driven significant progress in vision and text processing. However, achieving
human-like reasoning and interpretability in AI systems remains a substantial
challenge. The Neural-Symbolic paradigm, which integrates neural networks with
symbolic systems, presents a promising pathway toward more interpretable AI.
Within this paradigm, Knowledge Graphs (KG) are crucial, offering a structured
and dynamic method for representing knowledge through interconnected entities
and relationships, typically as triples (subject, predicate, object). This
paper explores recent advancements in neural-symbolic integration based on KG,
examining how it supports integration in three categories: enhancing the
reasoning and interpretability of neural networks with 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 future research directions in Neural-Symbolic AI.
