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.

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Elisa F. Kendall is a Partner in Thematix Partners LLC and graduate-level lecturer in computer science, focused on data management, data governance, knowledge representation, and decisioning systems. Her consulting practice includes business and information architecture, knowledge representation strategies, and ontology design, development, and training for clients in financial services, government, manufacturing, media, pharmaceutical, and retail domains. Recent projects have focused on use of ontologies to drive natural language processing, machine learning, interoperability, and other knowledge graph-based applications. Elisa represents knowledge representation, ontology, information architecture, and data management concerns on the Object Management Group (OMG)'s Architecture Board, is co-editor of the Ontology Definition Metamodel (ODM), and a contributor to a number of other ISO, W3C, and OMG standards, including the Financial Industry Business Ontology (FIBO) effort. Prior to joining Thematix, she was the founder and CEO of Sandpiper Software, an early entrant in the Semantic Web domain. Earlier in her career, she was software development manager for Aspect Development, and before that a ground systems data processing engineer for Lockheed Martin. She holds a B.S. in Mathematics and Computer Science from UCLA, and an A.M in Linguistics from Stanford University. Deborah L. McGuinness is the Tetherless World Senior Constellation Chair and Professor of Computer, Cognitive, and Web Sciences at RPI. She is also the founding director of the Web Science Research Center and the CEO of McGuinness Associates Consulting. Deborah has been recognized with awards as a fellow of the American Association for the Advancement of Science (AAAS) for contributions to the Semantic Web, knowledge representation, and reasoning environ-ments and as the recipient of the Robert Engelmore award from the Association for the Advancement of Artificial Intelligence (AAAI) for leadership inSemantic Web research and in bridging Artificial Intel-ligence (AI) and eScience, significant contributions to deployed AI applications, and extensive service to the AI community. Deborah leads a number of large diverse data intensive resource efforts and her team is creating next-generation ontology-enabled research infrastructure for work in large interdisciplinary settings. Prior to joining RPI, Deborah was the acting director of the Knowledge Systems, Artificial Intelligence Laboratory, and Senior Research Scientist in the Computer Science Department of Stanford University, and previous to that she was at AT&T Bell Laboratories. Deborah consults with numerous large corporations as well as emerging startup companies wishing to plan, develop, deploy, and maintain Semantic Web and/or AI applications. Some areas of recent work include: data science, next-generation health advisors, ontology design and evolution environments, semantically enabled virtual observatories, semantic integration of scientific data, context-aware mobile applications, search, eCommerce, configuration, and supply chain management. Deborah holds a Bachelor of Math and Computer Science from Duke University, a Master of Computer Science from University of California at Berkeley, and a Ph.D. in Computer Science from Rutgers University.