@conference {IT602,
	title = {IT602: A Semantic Web Representation of Entire Populations},
	booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)},
	series = {Proceedings of the Joint International Conference on Biological Ontology and BioCreative (2016)},
	year = {2016},
	month = {11/30/16},
	publisher = {CEUR-ws.org Volume 1747},
	organization = {CEUR-ws.org Volume 1747},
	abstract = {<p>Accurately representing demographic realities is a critical component in creating useful, agent-based epidemiological models of infectious disease. Synthetic ecosystems are generated from Census data microsamples in a statistically-sound manner to maintain population-level demographic characteristics. These highly detailed representations of populations are the basis of many advanced simulations of infectious disease epidemics. Creating a standard, machine-readable representation of synthetic ecosystem data would enable easier use and integration with epidemic simulator software. Here we describe an ontology-based representation in Resource Description Framework (RDF) and Web Ontology Language (OWL) of version 1.0 of the 2010 U.S. Synthetic Population database by RTI International. Our representation draws upon applicable classes from several reference ontologies, including the Ontology of Medically Related Social Entities (OMRSE). After failing to find suitable ontological representations of several key data elements in the Synthetic Population dataset, we created new classes in OMRSE for representing employment status, employee roles, workplaces, residences, households, and age measurements. We loaded a test RDF dataset (structured according to ontologies in OWL) of synthetic individuals into a commercial triple store (Stardog) and validated the representation with SPARQL queries.</p>
},
	url = {http://ceur-ws.org/Vol-1747/IT602_ICBO2016.pdf},
	author = {Daniel Welch and Amanda Hicks and Josh Hanna and William Hogan}
}
