@inproceedings{588cc37d4e25494a84778cfc99bf3a2b,
title = "GraphSPARQL: A GraphQL Interface for Linked Data",
abstract = "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.",
keywords = "GraphQL, GraphSPARQL, knowledge graphs, linked data, SPARQL",
author = "Kevin Angele and Manuel Meitinger and Marc Bu{\ss}j{\"a}ger and Stephan F{\"o}hl and Anna Fensel",
note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 37th ACM/SIGAPP Symposium on Applied Computing, SAC 2022 ; Conference date: 25-04-2022 Through 29-04-2022",
year = "2022",
month = apr,
doi = "10.1145/3477314.3507655",
language = "English",
series = "Proceedings of the ACM Symposium on Applied Computing",
publisher = "Association for Computing Machinery",
pages = "778--785",
booktitle = "Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, SAC 2022",
}