Abstract

Time‑aware knowledge representation models are central to enabling temporal reasoning and decision support in intelligent systems that operate in dynamic environments where information changes over time. Traditional static knowledge representations lack mechanisms to encode temporal aspects such as event ordering, durative states, evolving contexts, and temporal constraints, limiting their utility in domains like planning, diagnosis, forecasting, and autonomous control. Time‑aware models integrate temporal dimensions into knowledge structures, enabling systems to represent when facts hold, how relationships evolve, and what sequences of events lead to particular outcomes. Prominent frameworks include temporal logics (e.g., Linear Temporal Logic, Interval Temporal Logic), dynamic Bayesian networks, temporal semantic networks, event calculus, situation calculus, and temporal extensions of ontologies. These models support reasoning over time, including prediction, explanation, inconsistency detection, and plan validation. This paper surveys foundational concepts and contemporary approaches to temporal knowledge representation, outlines a structured methodology for designing and applying time‑aware models in decision support systems, and discusses empirical findings from benchmark applications in healthcare, logistics, and autonomous systems. We highlight key advantages such as improved inference accuracy and richer explanatory power, as well as challenges related to complexity, scalability, and knowledge acquisition. Future research directions include integrating temporal representation with learning models, uncertainty handling, and human‑centric decision frameworks.