Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 13 May 2019 / Approved: 15 May 2019 / Online: 15 May 2019 (05:51:53 CEST)

How to cite: 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 Nutritional Epidemiologic Research Output: From Paper-Based Standards to Linked Content. Preprints 2019, 2019050178 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 Nutritional Epidemiologic Research Output: From Paper-Based Standards to Linked Content. Preprints 2019, 2019050178

Abstract

1) Background: The use of linked data in Semantic Web are promising approaches 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 epidemiologic research; 2) Methods: First, a scoping review was conducted to identify existing ontology terms for reuse in ONE. Second, existing data standards and manuscript reporting guidelines for nutritional epidemiology were converted into ontology, and the terms used in the standards were summarized and listed separately in a taxonomic hierarchy. Third, the ontologies of the nutritional epidemiologic standards, reporting guidelines and the core concepts were gathered in ONE. Three case studies were illustrated for its 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; 3) Results: Ontologies for “food and nutrition” (n=33), “disease and special population” (n=86), “data description” (n=21), “research description” (n=32) and “supplementary (meta) data description” (n=44) were reviewed and listed. ONE consists of 339 classes (79 new classes to describe nutrition data and 24 new classes to describe the content of nutrition manuscripts). The case studies demonstrated the application of ONE. 4) Conclusion: ONE is a resource to automate data integration, searching and browsing, and can be used to assess reporting completeness in nutritional epidemiology.

Subject Areas

ontology; nutritional epidemiology; minimal data information; data quality descriptors; study reporting guidelines; Semantic Web

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