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What Are Ontologies?
Attempts to synthesize and summarize what is known about the world date back thousands of years. Scholars and nonscholars alike have faced the problem of how to organize knowledge and to integrate new observations with what is already known. Box 2-1 presents a brief summary of some early efforts to organize knowledge.
Philosophers use the term ontology (literally, discourse on being) to describe efforts to classify or group ideas, particularly those related to the nature of existence. Scientists today use the word ontology to refer to efforts to organize knowledge in particular domains. Although there is no universal definition of a scientific ontology, a valuable working definition is an explicit, formal specification of a shared conceptualization—a systematic set of shared terms and an explication of their interrelationships.1 As a simple example, an ontology might define and categorize types of ice cream products, distinguishing among those served in vessels, in cones, and on sticks, as shown in Figure 2-1.
Ontologies are used in many different kinds of applications, including those for information integration, knowledge management, Semantic Web services, and enterprise application integration. Ontologies can be used in different ways depending on the nature of the problem at hand. For example, ontologies can be applied to improve information retrieval
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1 We generally use the term “ontology” to refer to a range of systems that have been developed to enumerate the essential entities in a discipline, though not all of the systems may meet the precise definition; see Chapter 3 in the full report for a detailed discussion.
systems by providing a common understanding of concepts that humans and computers can both use. Ontologies can also be applied to undergird automated reasoning systems by providing formal definitions for concepts and the relationships among them.
Ontologies may be specified in various ways, such as lists of controlled terms, thesauri, taxonomies, and formal representations in logic. All of these can represent explicit specifications of shared conceptualizations—although with different degrees of formality. Ontologies shape many aspects of human life, including media consumption, e-commerce, and the use of social media. For example, the ability to turn on Netflix and scroll through recommended movies depends in part on an ontology that the company has used to classify its content.
Ontological systems lie on a continuum of increasing semantic complexity. That is, classification systems designed for ontological purposes (the specification of definitions and relationships) may include weak semantics (such as a simple taxonomy that specifies only class–subclass relationships) or strong semantics (such as formal representation in a logic that allows developers to specify the properties of entities and constraints on those properties). Thus, in this report the term ontological systems can refer to those that may or may not meet the definition of ontology.
SOURCE: Roz Chast, The New Yorker © Condé Nast. Reprinted with permission. https://archives.newyorker.com/newyorker/1986-08-04/flipbook/CV1/
Figure 2-2 illustrates the spectrum of semantic specification used in the context of the behavioral sciences, showing where controlled lists, thesauri, loose hierarchies, and taxonomies fall. Controlled lists, such as a list of social and behavioral determinants of health, are enumerations of specifically defined terms that help to provide consistency for users of the list. Thesauri organize terms so that the grouping reflects relationships among the terms: closely related terms are situated near one another, although exact relationships are unspecified. Taxonomies expand on thesauri by also showing hierarchical, class-subclass relationships, such as parent-child relationships, but the concepts are only enumerated: the relationships between concepts are not expressed in formal axioms. Table 2-1 provides more information about the examples shown Figure 2-2.
The point of the continuum is to demonstrate that a variety of representation systems are used in the behavioral sciences and that these repre-
TABLE 2-1 Examples of Ontological Systems on a Continuum
| Ontology | Brief Description |
|---|---|
| Social and Behavioral Determinants of Health | A controlled list of defined terms related to behavioral, social, economic, environmental, and occupational factors. The list helps organize information and provides terminology for the causes of morbidity, mortality, and future well-being. |
| Thesaurus of Psychological Index Terms | A controlled list of standardized terms and definitions of psychological concepts with a loose hierarchy showing relationships to other terms. The controlled vocabulary allows for indexing, cataloging, and searching of psychological concepts. |
| Diagnostic and Statistical Manual of Mental Disorders (DSM) | A loose hierarchy of the behavioral phenotypic manifestation of mental disorders using a common language and standard criteria based on consensus. The DSM features descriptions of mental health conditions and use categories to offer a diagnostic tool for clinical practice and research. |
| Big Five Personality Traits | A suggested grouping (taxonomy) of personality traits. The grouping provides a model of the primary dimensions of individual differences in personality, and personality trait facets that form part of a primary dimension.a |
| Behavioral Change Intervention Ontology (BCIO) | A formally specified set of entities and their relationships that establishes a common language. BCIO is used to organize information in a form that enables efficient accumulation of knowledge and enables links to other knowledge systems. |
a The five traits are extraversion (or extroversion), agreeableness, openness, conscientiousness, and neuroticism.
sentation systems vary in their degree of semantic specification. Considering this variety and the importance of designing classification structures to suit the needs of the researchers in a particular domain, the committee found that it was not useful to try discern a strict cutoff below which a structure would not be considered an ontology or to classify known structures as ontologies or “non-ontologies.” Instead, we highlight that the structures that exist along this continuum in the behavioral sciences serve ontological purposes that are scientifically valuable. These examples are discussed in more detail below.
All ontologies provide a structure for the enumeration of the entities in some domain. They articulate formal decisions about what is known and, to varying degrees, about the relationships among the elements of what is known, and they provide a means for sharing these enumerations across diverse approaches and methodologies. It is in this sense that a range of systems may serve ontological purposes: an ontological system with strong semantics is not necessarily better than one with weaker semantics. The use of strong or weak semantics will fit a specific purpose or set of purposes, and each inevitably will reflect the relative immaturity or maturity of the domain it is designed to systematize.
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