Vec2SPARQL: integrating SPARQL queries and knowledge graph embeddings

Maxat Kulmanov, Senay Kafkas, Andreas Karwath, Alexander Malic, Georgios V. Gkoutos, Michel Dumontier, Robert Hoehndorf

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Recent developments in machine learning have led to a rise of large number of methods for extracting features from structured data. The features are represented as 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 and we make a Vec2SPARQL endpoint available at

Original languageEnglish
Article number12
Number of pages12
JournalCEUR Workshop Proceedings
Publication statusPublished - 13 Dec 2018
Event11th International Conference Semantic Web Applications and Tools for Life Sciences, SWAT4LS 2018 - Antwerp, Belgium
Duration: 3 Dec 20186 Dec 2018


  • Knowledge graph
  • Vector space

ASJC Scopus subject areas

  • Computer Science(all)


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