As technology becomes integral to our lives, its influence on decision making in smart cities, healthcare, and manufacturing is undeniable. However, challenges such as limited contextual awareness, domain knowledge, explainability of machine learning (ML), and issues of interoperability, data quality, and GDPR (General Data Protection Regulation) compliance in data sharing hinder effective decision making. This book addresses these critical challenges by exploring how the synergy of semantic technologies (SW), like ontologies and knowledge graphs, with or without ML, can overcome these challenges to improve decision making. Through real-world case studies in data sharing, manufacturing, and agriculture, it offers theoretical and practical insights and guidelines of how SW can enhance prediction accuracy, integrate domain knowledge, support ML explainability, and tackle interoperability, data quality, and GDPR challenges.