基于符号知识库和参数化知识库的推理：综述
Reasoning based on symbolic and parametric knowledge bases: a survey
AI总结：
该综述从知识库依赖视角出发，将知识库分为符号与参数化两类，系统梳理对应及融合的推理方法，指明增强机器推理能力、缩小人机智能差距的未来方向。
摘要：
推理是人类智能的基础，对问题解决、决策和批判性思维至关重要。推理指的是基于现有知识得出新结论的过程，可支撑临床诊断、基础教育、金融分析等各类应用。尽管已有大量综述对推理相关方法进行了梳理，但尚无研究从方法所依赖的知识库视角出发，对这些方法展开系统性调研。知识库的应用场景和存储格式都存在显著差异，因此从知识库视角研究推理方法，有助于我们更好地理解相关挑战与未来方向。为填补这一空白，本文首先将知识库分为符号知识库和参数化知识库两类：前者以人类可读的符号形式显式存储信息，后者则将知识隐式编码在参数中。随后，本文全面概述了基于符号知识库、参数化知识库以及同时结合两类知识库的推理方法。最后，本文指出了增强推理能力、缩小人类与机器智能差距的未来研究方向。
英文摘要：
Reasoning is fundamental to human intelligence, and critical for problem-solving, decision-making, and critical thinking. Reasoning refers to drawing new conclusions based on existing knowledge, which can support various applications like clinical diagnosis, basic education, and financial analysis. Though a good number of surveys have been proposed for reviewing reasoning-related methods, none of them has systematically investigated these methods from the viewpoint of their dependent knowledge base. Both the scenarios to which the knowledge bases are applied and their storage formats are significantly different. Hence, investigating reasoning methods from the knowledge base perspective helps us better understand the challenges and future directions. To fill this gap, this paper first classifies the knowledge base into symbolic and parametric ones. The former explicitly stores information in human-readable symbols, and the latter implicitly encodes knowledge within parameters. Then, we provide a comprehensive overview of reasoning methods using symbolic knowledge bases, parametric knowledge bases, and both of them. Finally, we identify the future direction toward enhancing reasoning capabilities to bridge the gap between human and machine intelligence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
