Using SPARQL to unify queries over data, ontologies, and machine learning models in the PhenomeBrowser knowledgebase

Thumbnail
Name:
paper-12.pdf
Size:
529.9Kb
Format:
PDF
Description:
Published Version
Download
Type
Conference Paper
Authors
Syed, Ali Raza cc
Kafkas, Senay cc
Kulmanov, Maxat cc
Hoehndorf, Robert cc
KAUST Department
Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences & Engineering (CEMSE) Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
KAUST Grant Number
FCC/1/1976-08-01
FCC/1/1976- 08-08
URF/1/3790-01-01
URF/1/4355-01-01
Date
2022-01-01
Permanent link to this record
http://hdl.handle.net/10754/676712

Metadata
Show full item record
Abstract
We have developed the PhenomeBrowser knowledge base to integrate phenotype associations from a variety of sources into a single knowledge base. We use SPARQL as a unifying query language to access RDF data, perform Description Logic queries over ontologies, and compute the semantic similarity between entities in the knowledge base.
Sponsors
Supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3790-01-01, URF/1/4355-01-01, FCC/1/1976-08-01, and FCC/1/1976- 08-08.
We acknowledge use of the resources of the KAUST Supercomputing Core Laboratories.
Publisher
CEUR-WS
Conference/Event name
13th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2022
Collections
Conference Papers; Bio-Ontology Research Group (BORG); Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Copyright © 2022 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Except where otherwise noted, this item's license is described as Copyright © 2022 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).