Ontologies in the Behavioral Sciences Accelerating Research and the Spread of Knowledge (2022) / Chapter Skim
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3 Understanding Ontologies
3 Understanding Ontologies
Pages 49-72
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From page 49... ...
DEFINING ONTOLOGY From the perspective of computer science, an ontology has been defined as a shared conceptualization (of the "objects, concepts, and other entities that are assumed to exist" in a particular domain) that is formally specified (Gruber, 1995, p.
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Although earlier computer scientists working on intelligent computer systems had identified the importance of ontology engineering as a component of their work (e.g., Regoczei and Plantinga, 1987) , Gruber and his colleagues clarified the role of ontologies in knowledge engineering and offered a solution to one of the identified technical limitations of shared, reusable knowledge-based systems.
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. OWL is a Semantic Web language recommended for use in ontology development by the World Wide Web Consortium.
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A CONTINUUM OF SEMANTIC SPECIFICATION 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.
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From page 53... ...
.4 Thus, we use the term ontological systems when referring to those that may or may not meet the definition of ontology or when that issue is relevant to the discussion. Figure 3-1 illustrates the spectrum of semantic specification used in the context of the behavioral sciences, showing where controlled lists, thesauri, loose hierarchies, and taxonomies fall.
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Diagnostic A loose hierarchy of the behavioral phenotypic manifestation of and Statistical mental disorders using a common language and standard criteria Manual of Mental based on consensus. The DSM features descriptions of mental health Disorders (DSM)
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However, there are often times when an ontology requires strong semantics and the attendant complexity of a formal logic to address a nuanced problem. For example, the use of strong semantics may be necessary to standardize and align related measures that may otherwise be unclear or imprecise.
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A Formally Specified Ontology: The Behavioral Change Intervention Ontology There are very few ontological systems in the behavioral sciences that have been developed using standard representation languages such as OWL. While the Behavioral Change Intervention Ontology (BCIO)
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. To achieve rapid retrieval of this sort, it was necessary to organize evidence on behavior change interventions ontologically, in a formal and explicit way.
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The next step in the development was an expert feedback phase, during which experts were asked to rate the extent to which entity names were clear, definitions were nonoverlapping and without redundancy, relationships were suitable, and the overall structure was clear.11 This feedback was discussed and incorporated into further refinements of definitions until consensus was reached, and the results were shared with a wider team that included systems architects and computer scientists.12 The resulting behavioral interventions ontology has two types of behavioral change interventions, each with its own set of associated entities. The entities are related by 19 ontological relationships, such as has part, subclass of, has attribute, 10 See https://basic-formal-ontology.org/ 11 The feedback report is available as extended data at https://osf.io/yj235/; also see West et al.
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The BCIO, they suggest, helps structure thinking and communication about behavior change interventions. This structure can help researchers to identify knowledge gaps, to develop fruitful lines of inquiry, and to evaluate protocols.
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The development and updating of the DSM are based on expert review carried out by workgroups, and 13 See https://www.psychiatry.org/psychiatrists/practice/dsm/history-of-the-dsm for an ac count of the history of the classification of mental disorders in the United States (American Psychiatric Association, n.d.)
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All the diagnoses in the anxiety disorder class differ from diagnoses in the other classes on the basis of distinguishing characteristics of each class. For instance, the anxiety disorders class is distinguished from the class of depressive disorders based on the fact that the central defining feature of depressive disorders is the "presence of sad, empty or irritable mood, accompanied by somatic and cognitive changes that significantly affect the individual's capacity to function" (American Psychiatric Association, 2013, p.
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. For classifications of mental disorders to follow this approach, it would be necessary to link the observed signs and symptoms to bodily (brain)
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A Dimensional Classification System: Research Domain Criteria Tying observed or self-reported signs and symptoms at the behavioral phenotypic level more closely to biological processes was the objective when the National Institute of Mental Health (NIMH) sought to develop a new system for categorizing mental disorders.
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that cut across multiple DSM categories, rather than being constrained by the need to design research that aligned within DSM categories. From the perspective of NIMH, RDoC is intended to facilitate research that investigates fundamental dimensions, grounded in biology, that span multiple disorders (e.g., response to threat, attention, social processes)
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. The researchers applied natural language processing and machine learning techniques to the results of human neuroimaging collected over 25 years in order to redefine mental constructs in relation to brain activation data.
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Second, the natural organization of psychopathology can be discerned in the co-occurrence of its features: that is, using factor analytic methods, the underlying dimensions that organize psychopathology can be discovered by evaluating how signs and symptoms of psychopathology covary. Third, psychopathology can be organized hierarchically from narrow to broad dimensions so that specific psychopathology dimensions aggregate into more general factors.
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That is, use of factor analysis does not require shared conceptualization as an input, and the product of the factor analysis need not be anything that has a straightforward interpretation in terms of familar concepts: such interpretation involves the additional step of labeling the factors. CONCLUSIONS Both the DSM and RDoc are based in shared conceptualizations of phenomena, although they both lack formal specification of their content.
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From page 68... ...
Systems that fall along this continuum serve ontological purposes that are scientifi cally valuable. CONCLUSION 3-2: The classification systems that currently are widely used in the behavioral sciences do not have formal semantics, and therefore they do not readily provide opportunities to support automated reasoning and other artificial intelligence applications.
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. An information artifact ontology perspective on data collections and associated representational artifacts.
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: Toward a new classification frame work for research on mental disorders. The American Journal of Psychiatry, 167(7)
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. Research domain criteria: Strengths, weaknesses, and potential alternatives for future psychiatric research.
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