  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.
