Abstract

Cognitive computing has emerged as a transformative paradigm in semantic information retrieval and knowledge discovery by simulating human‑like reasoning, contextual understanding, and adaptive learning. Traditional information retrieval (IR) systems rely heavily on keyword matching and statistical co‑occurrence, often failing to capture deeper semantic relationships and user intent. Cognitive computing integrates symbolic reasoning, contextual awareness, ontologies, and machine learning to enable richer interpretation of textual content and complex queries. This paper explores cognitive models and architectures that enhance semantic retrieval accuracy and support knowledge discovery from structured and unstructured data. We examine foundational research in information retrieval, semantic memory modeling, and ontology‑based systems, tracing developments from early cognitive IR frameworks to more sophisticated semantic systems up to 2018. Through comprehensive literature synthesis, we identify key methodologies, conceptual frameworks, and performance trends. The advantages and limitations of cognitive approaches are discussed, especially in comparison to traditional IR models. Finally, we outline research methodologies for implementing cognitive semantic retrieval systems, present findings from applied studies, and suggest future research directions to advance the integration of cognitive computing and semantic knowledge discovery.