Ontologies in the Behavioral Sciences Accelerating Research and the Spread of Knowledge (2022) / Chapter Skim
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5 Engineering Behavioral Ontologies
5 Engineering Behavioral Ontologies
Pages 89-120
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EXISTING BEHAVIORAL ONTOLOGIES To review the current status of ontology design and implementation in the behavioral sciences, the committee commissioned a scoping review of the published literature on behavioral science ontologies (Falzon, 2021)
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.3 Falzon developed a list of 49 behavioral science ontologies identified in literature reviews, using a combination of subject headings and free text 1 The author of the scoping review cautioned that it is not definitive, noting that, although it was wide and covered multiple databases, the search terms used could have excluded relevant studies. She also noted that no formal appraisal of studies was conducted.
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to 41 (EmotionsOnto) , though the exact number of classes was unclear or unavailable for many of the ontologies reviewed.4 Several publications included in the scoping review described the development of single behavioral science ontologies (Brenas et al., 2019; Gkoutos et al., 2012; Hicks et al., 2016; Jensen et al., 2013; Köhler et al., 2012; Woznowski et al., 2018)
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92 ONTOLOGIES IN THE BEHAVIORAL SCIENCES TABLE 5-1 Ontologies Identified in the Surveyed Literature Number of Ontology Classes/Terms Domains Source BEHAVIORAL 1. Standard Animal NA Animal behavior Gkoutos Behavior Ontology et al., 2015 2. Neuro Behavior Ontology 1,036 Behavioral processes and Norris et al., phenotypes 2019 3. Health Behaviour Change 92 Behavior change and Norris et al., Ontology automated dialogue 2019 systems 4. Behaviour Change 110 Behavior change Blanch et al., Techniques 2017 5. Persuasion Support NA Behavior change Win et al., Systems for Health 2019 Behavior Change 6. Ontology of Behavior NA Behavior change Bickmore Change Counseling counseling et al., 2011 Concepts 7. Ontology of NA Self-regulation Eisenberg Self-Regulation et al., 2018 8. Cognitive Atlas 3,639 Cognitive neuroscience Norris et al., and mental processes 2019 9. Cognitive Paradigm 400 Cognitive and behavioral Norris et al., Ontology experiments 2019 10. EmotionsOnto 41 Emotions Norris et al., 2019 11. Emotion Ontology 902 Emotions Norris et al., 2019 12. Exposure Ontology 148 Exposure science, Norris et al., genomics and toxicology 2019 13. Lifestyle Ontology NA Life-style concepts Benmimoune et al., 2015 14. OntoPsychia 1,450 Social and environmental Blanch et al., determinants for 2017 psychiatry 15. Semantic Mining of Activity, 87 Health care data and Norris et al., Social and Health Data sustained weight loss 2019 16. Mental Functioning 692 Mental functioning and Norris et al., Ontology mental processes 2019 PHENOTYPES 17. Autism Spectrum 284 Autism spectrum Amith et al., Disorder Phenotype disorder phenotype 2018
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ENGINEERING BEHAVIORAL ONTOLOGIES 93 TABLE 5-1 Continued Number of Ontology Classes/Terms Domains Source 18. Human Phenotype 13,000 Phenotypes Gkoutos Ontology et al., 2015 19. Mammalian Phenotype 1,528 Phenotypes Köhler et al., Ontology 2012 20. Phenotype and Exposures 533 Phenotypes Blanch et al., 2017 21. Measurement Method 701 Methods used to Yu and Shen, Ontology make qualitative and 2016 quantitative clinical and phenotype measurement 22. Phenotype and Trait 5,607 Biodiversity and ecology, Köhler et al., Ontology plant phenotypes and traits 2012 DISEASE AND MENTAL HEALTH CONDITIONS 23. Disease Ontology NA Disease Gkoutos et al., 2015 24. Human Disease Ontology 12,498 Disease Norris et al., 2019 25. Symptom Ontology 942 Symptom and disease Norris et al., 2019 26. Alzheimer's Disease 1565 Alzheimer's disease Amith et al., Ontology 2018 27. Bilingual Ontology of 5,899 Alzheimer's disease Amith et al., Alzheimer's Disease and 2018 Related Diseases 28. National Cancer Institute 167,138 Cancer Blaum et al., Thesaurus 2013 29. Advancing Clinico-genomic NA Cancer research and Brochhausen Trials on Cancer – Open management et al., 2011 Grid Services for Improving Medical Knowledge Discovery (ACGT) Master Ontology 30. Adolescents' Depression 419 Depression Jung et al., Ontology 2016 31. Epidemiology Ontology 191 Epidemiology Norris et al., 2019 32. Epilepsy and Seizure NA Epilepsy and seizure Yu and Shen, Ontology 2016 33. Mental Disease Ontology 1,127 Mental disease Norris et al., 2019 continued
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94 ONTOLOGIES IN THE BEHAVIORAL SCIENCES TABLE 5-1 Continued Number of Ontology Classes/Terms Domains Source 34. Haghighi-Koeda Mood NA Mood disorder Yu and Shen, Disorder Ontology 2016 35. Neurological Disease 700 Neurological disease and Jensen et al., Ontology phenotypes 2013 GENETICS 36. Gene Ontology 43,850 Genetics Blaum et al., 2013 37. Micro Array Gene NA Microarray data and Wu and Expression Data experiments Yamaguchi, Ontology 2014 38. Ontology for Genetic 127 Genomic and proteomic Amith et al., Susceptibility health 2018 39. Pharmacogenetics 229 Pharmacogenetics Amith et al., Relationships Ontology 2018 NEUROSCIENCE 40. Biomedical Informatics 3,580 Neurons and neuronal Hastings and Research Network Project systems Schultz, 2012 Lexicon 41. Chemical Entities of 165,081 Neurotransmitters Hastings and Biological Interest Schultz, 2012 42. Consortium for NA Neuropsychiatric Blanch et al., Neuropsychiatric disorders 2017 Phenomics 43. OntoNeuroLOG 1016 Neuroimaging Blanch et al., 2017 44. Neural Electromagnetic 1,851 Biological process Blanch et al., Ontology 2017 45. Neuroinformatics NA Neuroinformatics Gkoutos Network et al., 2015 46. Neuroimaging Data 161 Neuroimaging Blanch et al., Model 2017 47. Neuropsychological NA Neuropsychological Gkoutos Testing Ontology testing et al., 2015 48. NeuroLex NA Neurons and neuronal Hastings and systems Schultz, 2012 49. Neuroscience Information 124,337 Neuroscience Blanch et al., Framework Ontology 2017 NOTE: NA means not available. SOURCE: Falzon (2021, Table 1)
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review the problems an ontology can help to solve and use ongoing work on ontologies related to behavior change to discuss key steps in ontology development. Several other studies identified by the scoping review discuss best practices and lessons learned.
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The authors of many of the studies discussed in the scoping review offer strong reasons why progress in ontology development and use would be a boost in the behavioral sciences (e.g., Poldrack and Yarkoni, 2016; Hastings and Schultz, 2012; Blanch et al., 2017; Larsen et al., 2017; Norris et al., 2019)
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It was also clear that efforts to date in the behavioral sciences have not yet by any means taken full advantage of the potential benefits of ontology development. While a systematic survey of existing behavioral ontologies was well beyond the scope of this study, we did identify some trends across the ontological systems we examined, including key examples from biomedicine.
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A common emphasis on and approach to creating ontologies in the behavioral sciences would likely reduce the chances of unhelpful pluralism. There have been some efforts to develop design patterns to support ontology creation -- relatively formal specifications of characteristic patterns (Blomqvist and Sandkuhl, 2005; Hitzler et al., 2016)
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The mappings from objects in the world to elements in the ontology can be very complex and sensitive to context, particularly in the behavioral sciences. The formal specification alone is insufficient to determine how to use an ontology.
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Change and Evolution Socio-cognitive practices also play a key role in ontology change and evolution. Because ontologies are formal specifications, it is easy to mistake them for relatively static structures.
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All of these instances of ontology change involve socio-cognitive practices and factors. For example, an ontology might change through open discussion and debate in the relevant scientific communities, as researchers and clinicians frequently work to revise and refine their shared ontologies (and understandings, more generally)
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Similarly, advances in ontology creation, dissemination, and use in the behavioral sciences may require institutional changes, including incentives of various types. The committee could not identify any existing institutional structures or incentives that directly promote the use of shared ontologies in the behavioral sciences.
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BOX 5-2 Gruber's Criteria for Developing Ontologies Intended for Reuse 1. Clarity.
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However, the efficiencies they provide, not to mention the capacities they afford for working with large bodies of data, have made them essential in much of behavioral science, and likely for the development and use of behavioral ontologies. Modern scientific ontologies may contain many thousands or even many millions of terms and are correspondingly complex, so technology has become essential for managing them.
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, specifically, machine learning, could be used to support ontology development, but there is little evidence so far about its broad utility for this purpose. A comprehensive review of available computational tools is beyond the scope of this report, but three key elements of the life-cycle of an ontology illustrate the contributions of computer technology (Noy et al., 2010)
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.7 Other tools are available: see Box 5-3. And there are commercial systems, such as TopQuadrant's TopBraid Composer8 and OWLGrEd.9 These are not particularly complex technologies -- they support ontology development in approximately the same way that word processing software supports the writing of a novel.
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. The second automated approach that can aid ontology creation is to use NLP to analyze large amounts of text or documents.
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. As noted above, the people working on ontology engineering hope that formal development methods or the use of design patterns -- as in conventional software engineering -- will greatly facilitate the early stages of ontology development (Corcho et al., 2006)
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. Another emerging trend related to ontology evaluation is the increasing use of NLP to generate semantic definitions through a natural language generation task, which can parse the ontologies and generate natural language text so that humans can assess its quality.
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Reasoners are also useful when there is uncertainty about whether the statements made by the ontology result in implications that are logically correct. Potential Directions for the Future The committee focused on the functions that computational tools currently provide, but there is considerable potential for future systems to provide additional kinds of support for ontology development and use.
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These ideas may bear fruit in the future, but we emphasize that current technology is already supporting ontology development and use and is currently more than sufficient to support progress in the behavioral sciences.
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The committee believes that tools and practices developed in other contexts are likely to be valuable to behavioral scientists as they pursue ontology development, but we acknowledge that there is as yet no empirical demonstrations of how they might work in the behavioral science domain. Iterative evaluation and testing of methods applied in new contexts will need to be integrated in the broader evaluations discussed above.
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There is a need for additional resources to increase awareness and training regarding ontologies in the behavioral sciences. There are many individuals in library and information sciences, for example, who have experience with ontology creation and use in other domains but may not have opportunities or time to work with behavioral scientists.
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CONCLUSION 5-4: For ontology engineering to progress in the behavioral sciences, sustained resources and specific actions and processes are needed in three areas: • discovery both foundational and translational research needed to develop and improve effective practices and the next generation of computational tools for ontology engi neering in the behavioral sciences.
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. Patterns in ontology engineering: Classification of ontology patterns.
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Paper prepared for the Committee on Accelerating Behavioral Science Through Ontology Development and Use, National Academies of Sciences, Engineering, and Medicine. https://nap.national academies.org/resource/26464/Falzon-comissioned-paper.pdf Franco, M., Vivo, J.M., Quesada-Martínez, M., Duque-Ramos, A., and Fernández-Breis, J.T.
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. Ontology Engineering with Ontol ogy Design Patterns: Foundations and applications, 25.
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. A scoping review of ontologies related to human behaviour change.
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: A proposed infrastructure for the ontology development lifecycle. http://ontolog.cim3.
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