Deborah L. McGuinness

Ontologies have become increasingly important as the use of knowledge graphs, machine learning, natural language processing (NLP), and the amount of data generated on a daily basis has exploded. As of 2014, 90% of the data in the digital universe was generated in the two years prior, and the volume of data was projected to grow from 3.2 zettabytes to 40 zettabytes in the next six years. The very real issues that government, research, and commercial organizations are facing in order to sift through this amount of information to support decision-making alone mandate increasing automation. Yet, the data profiling, NLP, and learning algorithms that are ground-zero for data integration, manipulation, and search provide less than satisfactory results unless they utilize terms with unambiguous semantics, such as those found in ontologies and well-formed rule sets. Ontologies can provide a rich "schema" for the knowledge graphs underlying these technologies as well as the terminological and semantic basis for dramatic improvements in results. Many ontology projects fail, however, due at least in part to a lack of discipline in the development process. This book, motivated by the Ontology 101 tutorial given for many years at what was originally the Semantic Technology Conference (SemTech) and then later from a semester-long university class, is designed to provide the foundations for ontology engineering. The book can serve as a course textbook or a primer for all those interested in ontologies.

This book is designed to provide the foundations for ontology engineering. It is motivated by the Ontology 101 tutorial given for many years at the Semantic Technology Conference and then later from a semester-long university class. The book can serve as a course textbook or a primer for all those interested in ontologies.Ontologies have become increasingly important as the use of knowledge graphs, machine learning, natural language processing (NLP), and the amount of data generated on a daily basis has exploded. As of 2014, 90% of the data in the digital universe had been generated in the preceding two years, and the volume of data was projected to grow from 3.2 zettabytes to 40 zettabytes in the following six years. The very real issues that government, research, and commercial organizations are facing in order to sift through this amount of information to support decision-making alone mandate increasing automation. Yet, the data profiling, NLP, and learning algorithms that are ground-zero for data integration, manipulation, and search provide less-than-satisfactory results unless they utilize terms with unambiguous semantics, such as those found in ontologies and well-formed rule sets. Ontologies can provide a rich "schema" for the knowledge graphs underlying these technologies as well as the terminological and semantic basis for dramatic improvements in results. Many ontology projects fail, however, due at least in part to a lack of discipline in the development process.

This monograph is a call to action. It is an urgent request to those using the web to help find solutions to problems facing healthcare globally. The disciplines of Web Science and its subfield Health Web Science explore how the World Wide Web drives discussions, technologies, policies, and solutions, which surround health issues.The monograph presents Health Web Science as a sub-discipline of Web Science that, while being concerned with the Web's impact on health and wellbeing, also examines the impact of the Web's health-related uses on the design, structure, and evolution of the Web itself. Understanding and appreciating the overlapping, yet divergent disciplinary orientation of Health Web Science compared to related research domains, motivates specific research efforts around better utilization of, innovation on, and communication over and within the Web.Health Web Science is an ideal reference for researchers and practitioners with an interest in this emerging topic.