GraphSPARQL: A GraphQL Interface for Linked Data

Kevin Angele, Manuel Meitinger, Marc Bußjäger, Stephan Föhl, Anna Fensel

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

5 Citations (Scopus)


In recent years, knowledge graphs have become widely adopted for storing and managing vast amounts of data, powering various applications. However, SPARQL as the query language for accessing those knowledge graphs has a steep learning curve and is too complex for many use cases. This paper presents GraphSPARQL, a middleware that allows accessing arbitrary SPARQL endpoints by using GraphQL, supporting the GraphQL operations query and mutation. GraphSPARQL abstracts the complexity of SPARQL without losing the ability to address classes and properties of distinct ontologies. Additionally, GraphSPARQL's extension to GraphQL allows using SPARQL filter operations to filter the data in queries. The evaluation showed that GraphSPARQL can compete with existing GraphQL to SPARQL solutions and outperforms them for deeply nested queries.

Original languageEnglish
Title of host publicationProceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, SAC 2022
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Electronic)9781450387132
Publication statusPublished - Apr 2022
Event37th ACM/SIGAPP Symposium on Applied Computing, SAC 2022 - Virtual, Online
Duration: 25 Apr 202229 Apr 2022

Publication series

NameProceedings of the ACM Symposium on Applied Computing


Conference37th ACM/SIGAPP Symposium on Applied Computing, SAC 2022
CityVirtual, Online


  • GraphQL
  • GraphSPARQL
  • knowledge graphs
  • linked data


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