Computational Semantics - Bridging Language, Logic, and Learning explores how intelligent systems derive meaning from data by combining formal logic, natural language processing, and machine learning. This edited volume offers a multidisciplinary perspective on semantic modeling, knowledge representation, interpretability, and reasoning in artificial intelligence. It presents both the theoretical foundations and modern applications of computational semantics across deep learning, information extraction, cognitive linguistics, adversarial machine learning, and AI ethics. Readers will gain insight into how semantics underpins intelligent decision-making, influences ethical frameworks, and enhances human–computer interaction. The book’s strength lies in its ability to connect abstraction and mathematical precision with pragmatic approaches to designing robust, explainable AI systems. Researchers, practitioners, graduate students, and developers in AI, computational linguistics, data science, and logic-based modeling will benefit from the volume’s comprehensive perspective and its emphasis on structure, interpretability, and semantic consistency. By connecting fundamental logic with data-informed learning methods, the book provides insightful guidance for improving the design of semantic-aware models that can reason, generalize, and communicate more naturally.