Standards > terminology artifact > DOI:10.25504/FAIRsharing.dpkb5f

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ready Semanticscience Integrated Ontology


General Information
The semanticscience integrated ontology (SIO) provides a simple, integrated upper level ontology (types, relations) for consistent knowledge representation across physical, processual and informational entities. It provides vocabulary for the Bio2RDF ( and SADI ( projects.

How to cite this record: SIO; Semanticscience Integrated Ontology; DOI:; Last edited: Feb. 22, 2018, 2:09 p.m.; Last accessed: Mar 24 2018 7:45 a.m.

This record is maintained by micheldumontier  ORCID

Record updated: July 6, 2017, 3:50 p.m. by The FAIRsharing Team.

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Additional Information

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Conditions of Use

Applies to: Data use


The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery.

Dumontier M, Baker CJ, Baran J, Callahan A, Chepelev L, Cruz-Toledo J, Del Rio NR, Duck G, Furlong LI, Keath N, Klassen D, McCusker JP, Queralt-Rosinach N, Samwald M, Villanueva-Rosales N, Wilkinson MD, Hoehndorf R.
J Biomed Semantics 2014

View Paper (PubMed) View Paper (DOI)

SIO Ontology Display

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Disclaimer: This widget assumes the availability of the ontology resources in the NCBO BioPortal.

Related Standards

Reporting Guidelines

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Models and Formats

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Implementing Databases (2)
DisGeNET: a database of gene-disease associations
DisGeNET is a discovery platform containing one of the largest collections available of genes and variants involved in human diseases. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models, and the scientific literature, and covers the whole landscape of human diseases. The current version (v4.0) contains 429,036 associations, between 17,381 genes and 15,093 diseases, disorders and clinical or abnormal human phenotypes, and 72,870 variant-disease associations between 46,589 SNPs and 6,356 diseases and phenotypes. The data are homogeneously annotated with controlled vocabularies and community-driven ontologies. Additionally, several original metrics are provided to assist the prioritization of genotype-phenotype relationships. The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages, and an R package.

Bio2RDF is an open-source project that uses Semantic Web technologies to build and provide the largest network of Linked Data for the Life Sciences. Bio2RDF defines a set of simple conventions to create RDF(S) compatible Linked Data from a diverse set of heterogeneously formatted sources obtained from multiple data providers.

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