The rapid advancement of semantic web technologies, along with the fact that they are at various levels of maturity, has left many practitioners confused about the current state of these technologies. Focusing on the most mature technologies, Applied Semantic Web Technologies integrates theory with case studies to illustrate the history, current state, and future direction of the semantic web. It maintains an emphasis on real-world applications and examines the technical and practical issues related to the use of semantic technologies in intelligent information management.

The book starts with an introduction to the fundamentals—reviewing ontology basics, ontology languages, and research related to ontology alignment, mediation, and mapping. Next, it covers ontology engineering issues and presents a collaborative ontology engineering tool that is an extension of the Semantic MediaWiki. Unveiling a novel approach to data and knowledge engineering, the text:

Using case studies, the book provides authoritative insights and highlights valuable lessons learned by the authors—information systems veterans with decades of experience. They explain how to create social ontologies and present examples of the application of semantic technologies in building automation, logistics, ontology-driven business process intelligence, decision making, and energy efficiency in smart homes.

The book presents a discussion of advanced novel technologies and their integration with intelligent sensor data processing. It focuses on optimization models for agriculture issues such as food traceability, supply chain management, and energy resource management. The text also covers case studies from agriculture-based companies to provide more practical exposure to the readers.

Key Features:

It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, agricultural science, and information technology.

The book presents a discussion of advanced novel technologies and their integration with intelligent sensor data processing. It focuses on optimization models for agriculture issues such as food traceability, supply chain management, and energy resource management. The text also covers case studies from agriculture-based companies to provide more practical exposure to the readers.

Key Features:

It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, agricultural science, and information technology.

There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms. This book sets its focus on CI based architectures, modeling, case studies and applications in big data analytics, and business intelligence. The intended audiences of this book are scientists, professionals, researchers, and academicians who deal with the new challenges and advances in the specific areas mentioned above. Designers and developers of applications in these areas can learn from other experts and colleagues through this book.

There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms. This book sets its focus on CI based architectures, modeling, case studies and applications in big data analytics, and business intelligence. The intended audiences of this book are scientists, professionals, researchers, and academicians who deal with the new challenges and advances in the specific areas mentioned above. Designers and developers of applications in these areas can learn from other experts and colleagues through this book.

The rapid advancement of semantic web technologies, along with the fact that they are at various levels of maturity, has left many practitioners confused about the current state of these technologies. Focusing on the most mature technologies, Applied Semantic Web Technologies integrates theory with case studies to illustrate the history, current state, and future direction of the semantic web. It maintains an emphasis on real-world applications and examines the technical and practical issues related to the use of semantic technologies in intelligent information management.

The book starts with an introduction to the fundamentals—reviewing ontology basics, ontology languages, and research related to ontology alignment, mediation, and mapping. Next, it covers ontology engineering issues and presents a collaborative ontology engineering tool that is an extension of the Semantic MediaWiki. Unveiling a novel approach to data and knowledge engineering, the text:

Using case studies, the book provides authoritative insights and highlights valuable lessons learned by the authors—information systems veterans with decades of experience. They explain how to create social ontologies and present examples of the application of semantic technologies in building automation, logistics, ontology-driven business process intelligence, decision making, and energy efficiency in smart homes.

The book presents a discussion of advanced novel technologies and their integration with intelligent sensor data processing. It focuses on optimization models for agriculture issues such as food traceability, supply chain management, and energy resource management. The text also covers case studies from agriculture-based companies to provide more practical exposure to the readers.

Key Features:

It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, agricultural science, and information technology.

The book presents a discussion of advanced novel technologies and their integration with intelligent sensor data processing. It focuses on optimization models for agriculture issues such as food traceability, supply chain management, and energy resource management. The text also covers case studies from agriculture-based companies to provide more practical exposure to the readers.

Key Features:

It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, agricultural science, and information technology.

There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms. This book sets its focus on CI based architectures, modeling, case studies and applications in big data analytics, and business intelligence. The intended audiences of this book are scientists, professionals, researchers, and academicians who deal with the new challenges and advances in the specific areas mentioned above. Designers and developers of applications in these areas can learn from other experts and colleagues through this book.

There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms. This book sets its focus on CI based architectures, modeling, case studies and applications in big data analytics, and business intelligence. The intended audiences of this book are scientists, professionals, researchers, and academicians who deal with the new challenges and advances in the specific areas mentioned above. Designers and developers of applications in these areas can learn from other experts and colleagues through this book.