SCRY: Enabling quantitative reasoning in SPARQL queries

A. Meroño-Peñuela; Bas Stringer; Antonis Loizou; Sanne Abeln; Jaap Heringa

SCRY: Enabling quantitative reasoning in SPARQL queries

A. Meroño-Peñuela, Bas Stringer, Antonis Loizou, Sanne Abeln, Jaap Heringa

DANS-KNAW

Onderzoeksoutput: Bijdrage aan conferentie › Poster › Wetenschappelijk

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The inability to include quantitative reasoning in SPARQL queries
slows down the application of Semantic Web technology in the life
sciences. SCRY, our SPARQL compatible service layer, improves this by executing
services at query time and making their outputs query-accessible, generating
RDF data on demand. The power of this approach is demonstrated with
two use cases, where we use SCRY to calculate standard deviations and to find
homologous proteins.

Originele taal-2	Engels
Aantal pagina's	2
Status	Gepubliceerd - 2015

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scry-enabling-quantitativeSubmitted manuscript, 187 KB

Census data open linked – CEDA_R From fragment to fabric – Dutch census data in a web of global cultural and historic information
Scharnhorst, A. (PI), Mandemakers, K. (Supervisor), van Harmelen, F. (Supervisor), Doorn, P. (Supervisor), Guéret, C. (Collaborator), Ashkpour, A. (Collaborator), Meroño-Peñuela, A. (Collaborator) & Schlobach, S. (Supervisor)
01/10/2011 → 31/03/2016
Project: Onderzoek

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T1 - SCRY: Enabling quantitative reasoning in SPARQL queries

AU - Meroño-Peñuela, A.

AU - Stringer, Bas

AU - Loizou, Antonis

AU - Abeln, Sanne

AU - Heringa, Jaap

PY - 2015

Y1 - 2015

N2 - The inability to include quantitative reasoning in SPARQL queriesslows down the application of Semantic Web technology in the lifesciences. SCRY, our SPARQL compatible service layer, improves this by executingservices at query time and making their outputs query-accessible, generatingRDF data on demand. The power of this approach is demonstrated withtwo use cases, where we use SCRY to calculate standard deviations and to findhomologous proteins.

AB - The inability to include quantitative reasoning in SPARQL queriesslows down the application of Semantic Web technology in the lifesciences. SCRY, our SPARQL compatible service layer, improves this by executingservices at query time and making their outputs query-accessible, generatingRDF data on demand. The power of this approach is demonstrated withtwo use cases, where we use SCRY to calculate standard deviations and to findhomologous proteins.

M3 - Poster

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SCRY: Enabling quantitative reasoning in SPARQL queries

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Projecten

Census data open linked – CEDA_R From fragment to fabric – Dutch census data in a web of global cultural and historic information

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