Logic-Based Knowledge Representation for Reasoning

Abstract

Logic-based knowledge representation (KR) plays a fundamental role in artificial intelligence by enabling machines to represent, interpret, and reason about knowledge in a formal and structured manner. Over the decades, logical formalisms such as propositional logic, first-order logic, description logics, and non-monotonic logics have been developed to address different reasoning requirements. This paper presents a comprehensive review of logic-based knowledge representation techniques and their role in reasoning systems. It discusses core logical frameworks, inference mechanisms, reasoning strategies, and practical applications in domains such as expert systems, semantic web, knowledge graphs, and intelligent agents. The strengths and limitations of each approach are critically examined, along with current research challenges including scalability, uncertainty handling, and integration with machine learning models. Tables are included to summarize logical formalisms and reasoning types. The study highlights that although statistical AI has gained prominence, logic-based reasoning remains crucial for explainability, transparency, and structured decision-making.

Keywords: Logic-based knowledge representation, reasoning systems, propositional logic, first-order logic, description logic, non-monotonic reasoning, semantic web, inference mechanisms.