DOI: 10.1101/463778Nov 7, 2018Paper

Vec2SPARQL: integrating SPARQL queries and knowledge graph embeddings

BioRxiv : the Preprint Server for Biology
Maxat KulmanovR. Hoehndorf

Abstract

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-r...Continue Reading

Citations

Oct 14, 2020·Briefings in Bioinformatics·Maxat KulmanovRobert Hoehndorf

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Software Mentioned

simGIC
Vec2SPARQL
Docker
Bio2Vec
PhenomeNET
SPARQL
Apache Jena
TransE
word2vec
Kaggle

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