The epidemiology ontology: an ontology for the semantic annotation of epidemiological resources

Abstract

BackgroundEpidemiology is a data-intensive and multi-disciplinary subject, where data integration, curation and sharing are becoming increasingly relevant, given its global context and time constraints. The semantic annotation of epidemiology resources is a cornerstone to effectively support such activities. Although several ontologies cover some of the subdomains of epidemiology, we identified a lack of semantic resources for epidemiology-specific terms. This paper addresses this need by proposing the Epidemiology Ontology (EPO) and by describing its integration with other related ontologies into a semantic enabled platform for sharing epidemiology resources.ResultsThe EPO follows the OBO Foundry guidelines and uses the Basic Formal Ontology (BFO) as an upper ontology. The first version of EPO models several epidemiology and demography parameters as well as transmission of infection processes, participants and related procedures. It currently has nearly 200 classes and is designed to support the semantic annotation of epidemiology resources and data integration, as well as information retrieval and knowledge discovery activities.ConclusionsEPO is under active development and is freely available at https://code.google.com/p/epidemiology-ontology/. We believe that the annotation of epidemiology resources with EPO will help researchers to gain a better understanding of global epidemiological events by enhancing data integration and sharing.

Fish Ontology framework for taxonomy-based fish recognition

“…The annotation of fish and fisheries resources in the FO and other related ontologies is a response to the emerging need for data sharing and integration especially for fish data resources ( Ashburner et al, 2000 ; Gangemi et al, 2004 ; Bizer et al, 2009 ; Dahdul et al, 2010 ; Dahdul et al, 2012 ; Midford et al, 2010 ; Midford et al, 2013 ; Federhen, 2011 ; Natale et al, 2011 ; Schriml et al, 2012 ; Tzitzikas et al, 2013 ; Van Slyke et al, 2014 ; Pesquita et al, 2014 ) and will be highly relevant for the future of fish and fisheries related research.…”

A scoping review of ontologies related to human behaviour change

“…Descriptions of the identified ontologies are provided in Table 1, with a brief description of each ontology available in Supplementary Notes 1 and graphs depicting related measures provided in Supplementary Figure 1. Ontologies represented areas of mental processes and cognitions 25,26,27 , mental 28,29 and physical disease 30,31,32 , psychological experimental design 33 , emotions 34,35 , epidemiology 36 and healthcare 37,38 . Identified ontologies were typically of medium scale, featuring between 100-1000 classes (entities; Table 1), with only the OBO Foundry-approved Human Disease Ontology (DOID) 30 having >10,000 classes (entities).…”

“…Nine out of 15 ontologies had clear natural language definitions for all terms (i.e non-overlapping terms which lack redundancy 24 ), such as 'Alcohol dependence' (DOID_0050741), defined as 'a substance addiction in which the substance that is compulsively consumed is alcohol' 30 . Ontologies were assessed as not having clear definitions if entities were mostly undefined 31 , or if definitions were extremely lengthy 36 . Graphs depicting measures of these quality assessments are provided in…”

Context Is Everything: Harmonization of Critical Food Microbiology Descriptors and Metadata for Improved Food Safety and Surveillance

“…Some examples include the Sequence Ontology (SO) (Eilbeck et al, 2005), the EDAM Bioinformatics Ontology (EDAM) (Ison et al, 2013), and DOID (Schriml et al, 2012), which describe sequences, genome assembly, and human disease. The Exposure, Epidemiology, Environment, Symptoms, and Transmission Ontologies (EXO, EPO, ENVO, SYMP, TRANS) describe types of exposures, facets of epidemiology, natural and built environments, clinical signs and symptoms, and modes of transmission (Mattingly et al, 2012; Pesquita et al, 2014; Buttigieg et al, 2016). Ontologies and other resources useful for genomic epidemiology are listed in Table 1 .…”