Knowledge-enhanced neural machine reasoning has garnered significant attention as a cutting-edge yet challenging research area with numerous practical applications. Over the past few years, plenty of studies have leveraged various forms of external knowledge to augment the reasoning capabilities of deep models, tackling challenges such as effective knowledge integration, implicit knowledge mining, and problems of tractability and optimization. However, there is a dearth of a comprehensive technical review of the existing knowledge-enhanced reasoning techniques across the diverse range of application domains. This survey provides an in-depth examination of recent advancements in the field, introducing a novel taxonomy that categorizes existing knowledge-enhanced methods into two primary categories and four subcategories. We systematically discuss these methods and highlight their correlations, strengths, and limitations. Finally, we elucidate the current application domains and provide insight into promising prospects for future research.

翻译：知识增强的神经机器推理作为一项前沿且富有挑战性的研究领域，凭借其众多实际应用而备受关注。近年来，大量研究利用各种形式的外部知识来增强深度模型的推理能力，应对诸如有效知识整合、隐式知识挖掘以及可计算性与优化等问题。然而，针对现有知识增强推理技术在多样化应用领域中的综合技术综述仍显匮乏。本文深入考察了该领域的最新进展，提出了一种新颖的分类法，将现有知识增强方法划分为两大类别和四个子类别。我们系统地讨论了这些方法，并揭示了它们之间的关联、优势与局限性。最后，我们阐明了当前的应用领域，并对未来有前景的研究方向提供了见解。