Ontology Engineering. Elisa F. Kendall

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      CHAPTER 1

       Foundations

      Ontologies have become increasingly important as the use of knowledge graphs, machine learning, and natural language processing (NLP), and the amount of data generated on a daily basis has exploded. Many ontology projects have failed, however, due at least in part to a lack of discipline in the development process. This book is designed to address that, by outlining a proven methodology for the work of ontology engineering based on the combined experience of the authors, our colleagues, and students. Our intent for this chapter is to provide a very basic introduction to knowledge representation and a bit of context for the material that follows in subsequent chapters.

1.1 BACKGROUND AND DEFINITIONS

      Most mature engineering fields have some set of authoritative definitions that practitioners know and depend on. Having common definitions makes it much easier to talk about the discipline and allows us to communicate with one another precisely and reliably about our work. Knowing the language makes you part of the club.

      We hear many overlapping and sometimes confusing definitions for “ontology,” partly because the knowledge representation (KR) field is still maturing from a commercial perspective, and partly because of its cross-disciplinary nature. Many professional ontologists have background and training in fields including formal philosophy, cognitive science, computational linguistics, data and information architecture, software engineering, artificial intelligence, or library science. As commercial awareness about linked data and ontologies has increased, people knowledgeable about a domain but not trained in any of these areas have started to build ontologies for use in applications as well. Typically, these individuals are domain experts looking for solutions to something their IT departments haven’t delivered, or they are enterprise architects who have run into brick walls attempting to use more traditional technologies to address tough problems. The result is that there is not as much consensus about what people mean when they talk about ontologies as one might think, and people often talk past one another without realizing that they are doing so.

      There are a number of well-known definitions and quotes in the knowledge representation field that practitioners often cite, and we list a few here to provide common grounding:

      “An ontology is a specification of a conceptualization.” (Gruber, 1993)

      This definition is one of the earliest and most cited definitions for ontology with respect to artificial intelligence. While it may seem a bit academic, we believe that by the time you finish reading this book, you’ll understand what it means and how to use it. It is, in fact, the most terse and most precise definition of ontology that we have encountered. Having said this, some people may find a more operational definition helpful:

      “An ontology is a formal, explicit description of concepts in a domain of discourse (classes (sometimes called concepts)), properties of each concept describing various features and attributes of the concept (slots (sometimes called roles or properties)), and restrictions on slots (facets (sometimes called role restrictions)).” (Noy and McGuinness, 2001)

      The most common term for the discipline of ontology engineering is “knowledge engineering,” as defined by John Sowa years ago:

      “Knowledge engineering is the application of logic and ontology to the task of building computable models of some domain for some purpose.” (Sowa, 1999)

      Any knowledge engineering activity absolutely must be grounded in a domain and must be driven by requirements. We will repeat this theme throughout the book and hope that the “of some domain for some purpose” part of John’s definition will compel our readers to specify the context and use cases for every ontology project you undertake. Examples of what we mean by context and use cases will be scattered throughout the sections that follow, and will be covered in depth in Chapter 3.

      Here are a few other classic definitions and quotes that may be useful as we consider how to model knowledge and then reason with that knowledge:

      “Artificial Intelligence can be viewed as the study of intelligent behavior achieved through computational means. Knowledge Representation then is the part of AI that is concerned with how an agent uses what it knows in deciding what to do.” (Brachman and Levesque, 2004)

      “Knowledge representation means that knowledge is formalized in a symbolic form, that is, to find a symbolic expression that can be interpreted.” (Klein and Methlie, 1995)

      “The task of classifying all the words of language, or what’s the same thing, all the ideas that seek expression, is the most stupendous of logical tasks. Anybody but the most accomplished logician must break down in it utterly; and even for the strongest man, it is the severest possible tax on the logical equipment and faculty.” (Charles Sanders Peirce, in a letter to editor B. E. Smith of the Century Dictionary)

      Our own definition of ontology is based on applied experience over the last 25–30 years of working in the field, and stems from a combination of cognitive science, computer science, enterprise architecture, and formal linguistics perspectives.

       An ontology specifies a rich description of the relevant to a particular domain or area of interest.

      • terminology, concepts, nomenclature;

      • relationships among and between concepts and individuals; and

      • sentences distinguishing concepts, refining definitions and relationships (constraints, restrictions, regular expressions)