This group explores how knowledge can be formally represented and logically processed in AI systems. It focuses on symbolic approaches that enable machines to reason over structured data and draw meaningful conclusions. Core research areas include formal languages such as Description Logics, OWL, and First-Order Logic; automated reasoning and inference; ontology design for domains like healthcare and manufacturing; hybrid neuro-symbolic methods; and applications in explainable AI and decision support.