Abstract

: Intelligent Information Systems (IIS) demand sophisticated mechanisms to represent, interpret, and reason about knowledge in dynamic, uncertain, and heterogeneous environments. Advanced Knowledge Representation (KR) and Automated Reasoning (AR) techniques form the backbone of such capabilities, enabling systems to model complex domains, draw logical inferences, resolve ambiguities, and support decision-making. This paper synthesizes the theoretical foundations, methodologies, and practical applications of contemporary KR and AR approaches, including semantic networks, ontologies, description logics, frame systems, rule-based engines, and constraint-based reasoning. We examine how these techniques empower IIS in diverse domains such as semantic search, expert systems, natural language understanding, and autonomous agents. The research integrates insights from logic, artificial intelligence, cognitive science, and database systems to highlight both strengths and limitations of existing models. We analyze hybrid frameworks that combine symbolic and sub-symbolic methods to address scalability and real-world variability. Methodologically, the study reviews algorithmic design, reasoning architectures, and performance evaluation. Results demonstrate that advanced KR and AR significantly enhance system interpretability and adaptability, while also introducing challenges in computational complexity and knowledge acquisition. We conclude with design recommendations and a roadmap for future research aimed at more intuitive, scalable, and context-aware intelligent information systems.