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dc.contributor.authorKulmanov, Maxat
dc.contributor.authorKafkas, Senay
dc.contributor.authorKarwath, Andreas
dc.contributor.authorMalic, Alexander
dc.contributor.authorGkoutos, Georgios
dc.contributor.authorDumontier, Michel
dc.contributor.authorHoehndorf, Robert
dc.date.accessioned2018-11-12T11:48:57Z
dc.date.available2018-11-12T11:48:57Z
dc.date.issued2018-11-08
dc.identifier.citationKulmanov M, Kafkas S, Karwath A, Malic A, Gkoutos G, et al. (2018) Vec2SPARQL: integrating SPARQL queries and knowledge graph embeddings. Available: http://dx.doi.org/10.1101/463778.
dc.identifier.doi10.1101/463778
dc.identifier.urihttp://hdl.handle.net/10754/629853
dc.description.abstractRecent developments in machine learning have lead to a rise of large number of methods for extracting features from structured data. The features are represented as a vectors and may encode for some semantic aspects of data. They can be used in a machine learning models for different tasks or to compute similarities between the entities of the data. SPARQL is a query language for structured data originally developed for querying Resource Description Framework (RDF) data. It has been in use for over a decade as a standardized NoSQL query language. Many different tools have been developed to enable data sharing with SPARQL. For example, SPARQL endpoints make your data interoperable and available to the world. SPARQL queries can be executed across multiple endpoints. We have developed a Vec2SPARQL, which is a general framework for integrating structured data and their vector space representations. Vec2SPARQL allows jointly querying vector functions such as computing similarities (cosine, correlations) or classifications with machine learning models within a single SPARQL query. We demonstrate applications of our approach for biomedical and clinical use cases. Our source code is freely available at https://github.com/bio-ontology-research-group/vec2sparql and we make a Vec2SPARQL endpoint available at http://sparql.bio2vec.net/.
dc.description.sponsorshipThis work was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01, FCC/1/1976-08-01, and FCS/1/3657-02-01.
dc.publisherCold Spring Harbor Laboratory
dc.relation.urlhttps://www.biorxiv.org/content/early/2018/11/07/463778
dc.rightsThe copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSPARQL
dc.subjectvector space
dc.subjectknowledge graph
dc.titleVec2SPARQL: integrating SPARQL queries and knowledge graph embeddings
dc.typePreprint
dc.contributor.departmentBio-Ontology Research Group (BORG)
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.eprint.versionPre-print
dc.contributor.institutionCentre for Computational Biology, University of Birmingham, Birmingham, UK
dc.contributor.institutionInstitute of Data Science,Maastricht University,Maastricht, The Netherlands
kaust.personKulmanov, Maxat
kaust.personKafkas, Senay
kaust.personHoehndorf, Robert
kaust.grant.numberURF/1/3454-01-01
kaust.grant.numberFCC/1/1976-08-01
kaust.grant.numberFCS/1/3657-02-01
dc.relation.issupplementedbygithub:bio-ontology-research-group/vec2sparql
refterms.dateFOA2018-11-12T12:33:46Z
display.relations<b>Is Supplemented By:</b><br/> <ul><li><i>[Software]</i> <br/> Title: bio-ontology-research-group/vec2sparql: SPARQL Endpoint with functions for computing embedding similarities. Publication Date: 2018-07-31. github: <a href="https://github.com/bio-ontology-research-group/vec2sparql" >bio-ontology-research-group/vec2sparql</a> Handle: <a href="http://hdl.handle.net/10754/668126" >10754/668126</a></a></li></ul>


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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Except where otherwise noted, this item's license is described as The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.