From Survey to Ontology, the challenges and opportunities in ontologizing questions and answers LaurenChan chanl@oregonstate.edu College of Public Health and Human Sciences Oregon State University
97331 Corvallis OR USA
JimmyPhuong Division of Biomedical Health and Informatics University of Washington Medicine
98195 Seattle WA USA
AnneThessen Department of Biomedical Informatics University of Colorado Anschutz Medical Campus
80054 Aurora CO USA
StephanieHong Section of Biomedical Informatics and Data Science School of Medicine Johns Hopkins University
21218 Baltimore MD USA
ShahimEssaid Department of Biomedical Informatics University of Colorado Anschutz Medical Campus
80054 Aurora CO USA
MelissaHaendel Department of Biomedical Informatics University of Colorado Anschutz Medical Campus
80054 Aurora CO USA
International Conference on Biomedical Ontology (ICBO 2022)
September 25-28 2022 Ann Arbor Michigan USA
From Survey to Ontology, the challenges and opportunities in ontologizing questions and answers 1613-0073 E05E4D2EF75A60F33A5255AE54C7335F GROBID - A machine learning software for extracting information from scholarly documents ontologies survey alignment common data elements data interoperability

Surveys are an essential tool for gathering data from individuals in a wide variety of fields. Common Data Elements (CDEs) are routinely used in surveys to streamline frequently asked questions and support question reuse. While CDEs are a widespread method for collecting survey data, they face a variety of problems including inconsistent file formats, lack of computational encoding, little to no community development standards, and frequent duplication of CDEs across various registries. Alternative approaches such as ontologies can serve as meaningful translation tools to not only support interoperability between similar/duplicate CDEs, but also to coordinate survey data with other ontology aligned data from heterogeneous methodologies including wet lab, clinical, or field research. To capitalize on the extensive data collected using CDEs, we propose a two-step approach of 1) aligning CDEs with ontology terminology to coordinate similar questions based on primary topic and 2) using standardized enumerations for CDE responses that can support data harmonization for meta analytics. Using this approach, greater data integration can support higher powered analytics and coordination of heterogeneous data types which are often difficult to study in tandem.