Reasoning based on symbolic and parametric knowledge bases: a survey

TL;DR

This survey systematically reviews reasoning methods based on symbolic and parametric knowledge bases, highlighting their differences, applications, and future challenges to improve AI reasoning capabilities.

Contribution

It classifies knowledge bases into symbolic and parametric types and provides a comprehensive overview of reasoning methods for each, addressing a gap in existing surveys.

Findings

Different knowledge base types require distinct reasoning approaches

Symbolic knowledge bases are human-readable and explicit

Parametric knowledge bases encode knowledge implicitly within parameters

Abstract

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.