Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 97-100).

1. Foundations -- 1.1. Background and definitions -- 1.2. Logic and ontological commitment -- 1.3. Ontology-based capabilities -- 1.4. Knowledge representation languages -- 1.5. Knowledge bases, databases, and ontology -- 1.6. Reasoning, truth maintenance, and negation -- 1.7. Explanations and proof

2. Before you begin -- 2.1. Domain analysis -- 2.2. Modeling and levels of abstraction -- 2.3. General approach to vocabulary development -- 2.4. Business vocabulary development -- 2.5. Evaluating ontologies -- 2.6. Ontology design patterns -- 2.7. Selecting a language

3. Requirements and use cases -- 3.1. Getting started -- 3.2. Gathering references and potentially reusable ontologies -- 3.3. A bit about terminology -- 3.4. Summarizing the use case -- 3.5. The "body" of the use case -- 3.6. Creating usage scenarios -- 3.7. Flow of events -- 3.8. Competency questions -- 3.9. Additional resources -- 3.10. Integration with business and software requirements

4. Terminology -- 4.1. How terminology work fits into ontology engineering -- 4.2. Laying the groundwork -- 4.3. Term excerption and development -- 4.4. Terminology analysis and curation -- 4.5. Mapping terminology annotations to standard vocabularies

5. Conceptual modeling -- 5.1. Overview -- 5.2. Getting started -- 5.3. Identifying reusable ontologies -- 5.4. Preliminary domain modeling -- 5.5. Naming conventions for web-based ontologies -- 5.6. Metadata for ontologies and model elements -- 5.7. General nature of descriptions -- 5.8. Relationships and properties -- 5.9. Individuals and data ranges -- 5.10. Other common constructs -- 6. Conclusion.

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

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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.

Also available in print.

Title from PDF title page (viewed on May 3, 2019).