Formal Methods For Managing And Processing Ontology Alignments
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Formal Methods for Managing and Processing Ontology Alignments
Ontologies are formal knowledge representation methods that can provide means for a shared understanding of a given domain. To enable meaningful communication and interoperability between two or more information systems that utilize independently created ontologies, a bridge between them is necessary, often referred to as an ontology alignment. Formal Methods for Managing and Processing Ontology Alignments provides complete ontology alignment lifecycle, including modelling, methods, and maintenance processes. Summarizing the author's research from the past ten years, this book consolidates findings previously published in prestigious international journals and presented at leading conferences. Each chapter is designed to be self-contained, allowing readers to approach the material modularly. The text introduces basic mathematical concepts, and later chapters build upon these foundations, but each focuses on specific aspects of ontology alignment, making the content accessible and easy to follow. Key Features Includes formal definitions of ontologies and ontology alignment along with a set of methods for providing semantics of attributes and relations. Provides application of fuzzy logic in the task of ontology alignment. Contains methods of managing the evolution of ontologies and their alignments. Proposes novel methods of assessing the quality ontology alignment. Situated in the fields of knowledge representation and semantic technologies, this book is an invaluable resource for both academic researchers and practitioners, as well as students looking to deepen their understanding.
Formal Approaches for Ontology Alignment Engineering
"Ontologies are formal knowledge representation methods that can provide means for a shared understanding of a given domain. To enable meaningful communication and interoperability between two or more information systems that utilize independently created ontologies, a bridge between them is necessary, often referred to as an ontology alignment. This book provides complete ontology alignment lifecycle, including modelling, methods, and maintenance processes. Summarizing the author's research from the past ten years, this book consolidates findings previously published in prestigious international journals and presented at leading conferences. Each book chapter is designed to be self-contained, allowing readers to approach the material modularly. The book introduces basic mathematical concepts, and later chapters build upon these foundations, but each focusses on specific aspects of ontology alignment, making the content accessible and easy to follow. Key Features: Includes formal definitions of ontologies and ontology alignment along with a set of methods for providing semantics of attributes and relations. Provides application of fuzzy logic in the task of ontology alignment. Contains methods of managing the evolution of ontologies and their alignments. Proposes novel methods of assessing the quality ontology alignment. Situated in the fields of knowledge representation and semantic technologies, this book is an invaluable resource for both academic researchers and practitioners, as well as students looking to deepen their understanding"--
Ontology Engineering Applications in Healthcare and Workforce Management Systems
Looking beyond the communications technology horizon and projecting future competency-specific employment demand, this book presents an evaluation of desirable information systems enhancements by integrating two disparate-domain computer ontologies. It provides readers a fresh solutions approach based on dynamic modeling and methodological contributions to philosophical and assistive communications system development in healthcare, addressing the need for both demand intelligence and practical work environment support. The pace of change in redefining occupation-specific employee resourcing needs is unrelenting and continues to accelerate. And the exponential growth in the demand for healthcare service delivery is correspondingly daunting. As such, the public and private sectors are faced with the challenge of sustaining credible relevant demand intelligence and recruitment practices, while integration, expansion and enrichment of ostensibly unconnected ontologies represent key R&D issues.