Improving Decision Making Using Semantic Web Technologies
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Beschreibung
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.
About the author
Dr. Tek Raj Chhetri is currently a postdoctoral associate at the Senseable Intelligence Group at the McGovern Institute for Brain Research at the Massachusetts Institute of Technology, United States. He is the founder and director of CAIR-Nepal (Center for Artificial Intelligence Research Nepal), an artificial intelligence research organization in Nepal. He conducts research in artificial intelligence, knowledge graphs, data privacy, and Internet of Things (IoT) data sharing.
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.
Autor*in
Tek Raj Chhetri
Themen in »Improving Decision Making Using Semantic Web Technologies«
Knowledge Graphs Explainable AI (XAI) Decision making Data Privacy General Data Protection Regulation (GDPR) compliance Data Sharing Artificial Intelligence (AI) Edge Intelligence Internet of Things (IoT)