An empirical meta-analysis of the life sciences linked open data on the web

Abstract While the biomedical community has published several “open data” sources in the last decade, most researchers still endure severe logistical and technical challenges to discover, query, and integrate heterogeneous data and knowledge from multiple sources. To tackle these challenges, the com...

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Autores principales:	Maulik R. Kamdar, Mark A. Musen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/b14fba1397c94b8f9f0034e2fc388580
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id	oai:doaj.org-article:b14fba1397c94b8f9f0034e2fc388580
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spelling	oai:doaj.org-article:b14fba1397c94b8f9f0034e2fc3885802021-12-02T14:09:02ZAn empirical meta-analysis of the life sciences linked open data on the web10.1038/s41597-021-00797-y2052-4463https://doaj.org/article/b14fba1397c94b8f9f0034e2fc3885802021-01-01T00:00:00Zhttps://doi.org/10.1038/s41597-021-00797-yhttps://doaj.org/toc/2052-4463Abstract While the biomedical community has published several “open data” sources in the last decade, most researchers still endure severe logistical and technical challenges to discover, query, and integrate heterogeneous data and knowledge from multiple sources. To tackle these challenges, the community has experimented with Semantic Web and linked data technologies to create the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we extract schemas from more than 80 biomedical linked open data sources into an LSLOD schema graph and conduct an empirical meta-analysis to evaluate the extent of semantic heterogeneity across the LSLOD cloud. We observe that several LSLOD sources exist as stand-alone data sources that are not inter-linked with other sources, use unpublished schemas with minimal reuse or mappings, and have elements that are not useful for data integration from a biomedical perspective. We envision that the LSLOD schema graph and the findings from this research will aid researchers who wish to query and integrate data and knowledge from multiple biomedical sources simultaneously on the Web.Maulik R. KamdarMark A. MusenNature PortfolioarticleScienceQENScientific Data, Vol 8, Iss 1, Pp 1-21 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Maulik R. Kamdar Mark A. Musen An empirical meta-analysis of the life sciences linked open data on the web
description	Abstract While the biomedical community has published several “open data” sources in the last decade, most researchers still endure severe logistical and technical challenges to discover, query, and integrate heterogeneous data and knowledge from multiple sources. To tackle these challenges, the community has experimented with Semantic Web and linked data technologies to create the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we extract schemas from more than 80 biomedical linked open data sources into an LSLOD schema graph and conduct an empirical meta-analysis to evaluate the extent of semantic heterogeneity across the LSLOD cloud. We observe that several LSLOD sources exist as stand-alone data sources that are not inter-linked with other sources, use unpublished schemas with minimal reuse or mappings, and have elements that are not useful for data integration from a biomedical perspective. We envision that the LSLOD schema graph and the findings from this research will aid researchers who wish to query and integrate data and knowledge from multiple biomedical sources simultaneously on the Web.
format	article
author	Maulik R. Kamdar Mark A. Musen
author_facet	Maulik R. Kamdar Mark A. Musen
author_sort	Maulik R. Kamdar
title	An empirical meta-analysis of the life sciences linked open data on the web
title_short	An empirical meta-analysis of the life sciences linked open data on the web
title_full	An empirical meta-analysis of the life sciences linked open data on the web
title_fullStr	An empirical meta-analysis of the life sciences linked open data on the web
title_full_unstemmed	An empirical meta-analysis of the life sciences linked open data on the web
title_sort	empirical meta-analysis of the life sciences linked open data on the web
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b14fba1397c94b8f9f0034e2fc388580
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An empirical meta-analysis of the life sciences linked open data on the web

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