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.
@article{arxiv.2302.02093,
title = {Knowledge-enhanced Neural Machine Reasoning: A Review},
author = {Tanmoy Chowdhury and Chen Ling and Xuchao Zhang and Xujiang Zhao and Guangji Bai and Jian Pei and Haifeng Chen and Liang Zhao},
journal= {arXiv preprint arXiv:2302.02093},
year = {2023}
}