Ontologies in the Behavioral Sciences: Accelerating Research and the Spread of Knowledge Digest Version (2022) / Chapter Skim
Currently Skimming:
5 Engineering Behavioral Ontologies
5 Engineering Behavioral Ontologies
Pages 25-34
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 25... ...
EXISTING BEHAVIORAL ONTOLOGIES The committee commissioned a scoping review of the published literature on behavioral science ontologies.1 The review was designed to provide an understanding of how and to what extent these ontologies have been studied; the significant findings from this body of work; and any discernable trends or patterns in the behavioral science ontologies that have been studied. The committee also hoped to gain insight into the methods by which ontologies have been created in the behavioral sciences and their strengths and limitations.
|
From page 26... ...
THE ONTOLOGY ENGINEERING PROCESS The development of scientific ontologies rests on two equally important components: human, socio-cognitive practices and decisions, and computational tools. Socio-Cognitive Practices Humans must make key decisions about the terms and relationships to be covered in an ontology.
|
From page 27... ...
Ontology change can also be driven by factors that are external to scientific communities, such as cultural changes in conceptualizations. Social expectations, needs, values, and concepts can all change over time, and so there can be pressure -- social, political, psychological -- to adjust a scientific ontology so that it is more consistent with those social factors.
|
From page 28... ...
They do not offer ready means of helping scientific communities determine what they need or of engaging colleagues in the socio-cognitive tasks detailed above. But technological tools do play an important supportive role in facilitating ontology design and use.
|
From page 29... ...
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. For this hope to be realized, many of these methods need to be empirically evaluated in the behavioral sciences and also built into general-purpose ontology development tools.
|
From page 30... ...
For example, the widely used ontology software library ROBOT offers a "report" function that runs a series of quality control tests over an input ontology and generates a report file based on the results, suitable for use in an automated workflow. 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.
|
From page 31... ...
There is a need for additional resources to increase awareness and training regarding ontologies in the behavioral sciences. In addition, full utilization of both current and future computational tools will require significant increases in computational resources including computer time, data access, and server storage.
|
From page 32... ...
CONCLUSION 7: To provide the intended benefits an ontology should be logically sound, valid, and usable: • logically sound -- contains no contradictions and is technically correct and concisely expressed in formal terms; • valid -- the definitions it provides accurately reflect the domain it covers as completely as possible; and • usable by a diverse range of stakeholders, depending on its purpose -- including scientists, practitioners, and others. CONCLUSION 8: 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 gen eration of computational tools for ontology engineering in the behavioral sciences.
|
From page 33... ...
ENGINEERING BEHAVIORAL ONTOLOGIES 33 • capacity to address shortfalls in implementation and to take advantage of the cases when novel research is not required -- that is, when what needs to be done is clear, but there is cur rently no capacity to do it. • promotion of practices and processes that could support wider use of ontologies in the behavioral sciences, and for which the capacity is already in place, but have not been widely deployed, such as institutional incentives, open data and code, and com munity-level efforts to bring consensus about ontologies in the behavioral sciences through collaboration.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.