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An Ontology to Standardize Research Output of Nutritional Epidemiology: From Paper-Based Standards to Linked Content

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Department of Food Technology, Safety and Health, Ghent University, 9000 Ghent, Belgium
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KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000 Ghent, Belgium
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Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO 65203, USA
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Netherlands Organization for Applied Scientific Research, NL-2509 Zeist, The Netherlands
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Department of Telecommunications and information processing, Ghent University, 9000 Ghent, Belgium
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ONTOFORCE, 9052 Ghent, Belgium
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Nutrients 2019, 11(6), 1300; https://doi.org/10.3390/nu11061300
Received: 13 May 2019 / Revised: 3 June 2019 / Accepted: 6 June 2019 / Published: 8 June 2019
Background: The use of linked data in the Semantic Web is a promising approach to add value to nutrition research. An ontology, which defines the logical relationships between well-defined taxonomic terms, enables linking and harmonizing research output. To enable the description of domain-specific output in nutritional epidemiology, we propose the Ontology for Nutritional Epidemiology (ONE) according to authoritative guidance for nutritional epidemiology. Methods: Firstly, a scoping review was conducted to identify existing ontology terms for reuse in ONE. Secondly, existing data standards and reporting guidelines for nutritional epidemiology were converted into an ontology. The terms used in the standards were summarized and listed separately in a taxonomic hierarchy. Thirdly, the ontologies of the nutritional epidemiologic standards, reporting guidelines, and the core concepts were gathered in ONE. Three case studies were included to illustrate potential applications: (i) annotation of existing manuscripts and data, (ii) ontology-based inference, and (iii) estimation of reporting completeness in a sample of nine manuscripts. Results: Ontologies for “food and nutrition” (n = 37), “disease and specific population” (n = 100), “data description” (n = 21), “research description” (n = 35), and “supplementary (meta) data description” (n = 44) were reviewed and listed. ONE consists of 339 classes: 79 new classes to describe data and 24 new classes to describe the content of manuscripts. Conclusion: ONE is a resource to automate data integration, searching, and browsing, and can be used to assess reporting completeness in nutritional epidemiology. View Full-Text
Keywords: ontology; nutritional epidemiology; minimal data information; data quality descriptors; study reporting guidelines; Semantic Web ontology; nutritional epidemiology; minimal data information; data quality descriptors; study reporting guidelines; Semantic Web
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MDPI and ACS Style

Yang, C.; Ambayo, H.; De Baets, B.; Kolsteren, P.; Thanintorn, N.; Hawwash, D.; Bouwman, J.; Bronselaer, A.; Pattyn, F.; Lachat, C. An Ontology to Standardize Research Output of Nutritional Epidemiology: From Paper-Based Standards to Linked Content. Nutrients 2019, 11, 1300. https://doi.org/10.3390/nu11061300

AMA Style

Yang C, Ambayo H, De Baets B, Kolsteren P, Thanintorn N, Hawwash D, Bouwman J, Bronselaer A, Pattyn F, Lachat C. An Ontology to Standardize Research Output of Nutritional Epidemiology: From Paper-Based Standards to Linked Content. Nutrients. 2019; 11(6):1300. https://doi.org/10.3390/nu11061300

Chicago/Turabian Style

Yang, Chen, Henry Ambayo, Bernard De Baets, Patrick Kolsteren, Nattapon Thanintorn, Dana Hawwash, Jildau Bouwman, Antoon Bronselaer, Filip Pattyn, and Carl Lachat. 2019. "An Ontology to Standardize Research Output of Nutritional Epidemiology: From Paper-Based Standards to Linked Content" Nutrients 11, no. 6: 1300. https://doi.org/10.3390/nu11061300

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