Vec2SPARQL: integrating SPARQL queries and knowledge graph embeddings
Type
PreprintAuthors
Kulmanov, Maxat
Kafkas, Senay

Karwath, Andreas

Malic, Alexander

Gkoutos, Georgios

Dumontier, Michel

Hoehndorf, Robert

KAUST Department
Bio-Ontology Research Group (BORG)Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant Number
URF/1/3454-01-01FCC/1/1976-08-01
FCS/1/3657-02-01
Date
2018-11-08Permanent link to this record
http://hdl.handle.net/10754/629853
Metadata
Show full item recordAbstract
Recent 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/.Citation
Kulmanov 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.Sponsors
This 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.Publisher
Cold Spring Harbor LaboratoryDOI
10.1101/463778Additional Links
https://www.biorxiv.org/content/early/2018/11/07/463778ae974a485f413a2113503eed53cd6c53
10.1101/463778
Scopus Count
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