Reasoning based on symbolic and parametric knowledge bases: a survey

2025-01-02

AI Summary

This study addresses the challenge of aligning machine reasoning with human cognition, focusing on synergistic modeling between symbolic and parametric knowledge bases. Method: We propose a unified analytical framework centered on knowledge base typology, introducing the first formal “symbolic–parametric” dichotomy theory and identifying hybrid reasoning as the critical pathway toward human–machine intelligence alignment. Integrating knowledge representation and reasoning (KRR), symbolic logic, neurosymbolic computing, implicit knowledge extraction from large language models, and knowledge fusion architectures, we construct a comprehensive methodology map for knowledge-base-driven reasoning across the full spectrum of paradigms. Contribution/Results: The work identifies three fundamental bottlenecks—interpretability, generalization, and dynamic knowledge updating—and establishes neurosymbolic integration as the pivotal direction for overcoming them, thereby advancing principled, aligned, and adaptive AI reasoning systems.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems

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.

Problem

• Symbolic Knowledge
• Parameter Database
• Human-like Reasoning

Innovation

• Symbolic Knowledge Bases
• Parametric Knowledge Bases
• Human-like Machine Reasoning