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极具挑战性的研究领域，因其丰富的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，应对有效知识整合、隐式知识挖掘以及可处理性与优化难题等挑战。然而，现有知识增强推理技术在不同应用领域中的全面技术综述仍然缺乏。本综述深入考察了该领域的最新进展，提出了一种新颖的分类体系，将现有知识增强方法划分为两个主要类别和四个子类别。我们系统性地讨论了这些方法，并强调了它们之间的关联、优势及局限性。最后，我们阐述了当前的应用领域，并对未来的研究前景提出了富有洞见的展望。