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Exploiting Declarative Mapping Rules for Generating GraphQL Servers with Morph-GraphQL

    https://doi.org/10.1142/S0218194020400070Cited by:4 (Source: Crossref)
    This article is part of the issue:

    In the last decade, REST has become the most common approach to provide web services, yet it was not originally designed to handle typical modern applications (e.g. mobile apps). GraphQL was proposed to reduce the number of queries and data exchanged in comparison with REST. Since its release in 2015, it has gained momentum as an alternative approach to REST. However, generating and maintaining GraphQL resolvers is not a simple task. First, a domain expert has to analyze a dataset, design the corresponding GraphQL schema and map the dataset to the schema. Then, a software engineer (e.g. GraphQL developer) implements the corresponding GraphQL resolvers in a specific programming language. In this paper, we present an approach to exploit the information from mappings rules (relation between target and source schema) and generate a GraphQL server. These mapping rules construct a virtual knowledge graph which is accessed by the generated GraphQL resolvers. These resolvers translate the input GraphQL queries into the queries supported by the underlying dataset. Domain experts or software developers may benefit from our approach: a domain expert does not need to involve software developers to implement the resolvers, and software developers can generate the initial version of the resolvers to be implemented. We implemented our approach in the Morph-GraphQL framework and evaluated it using the LinGBM benchmark.

    References

    • 1. M. Bryant , GraphQL for archival metadata: An overview of the EHRI GraphQL API, in IEEE Int. Conf. Big Data, 2017, pp. 2225–2230. CrossrefGoogle Scholar
    • 2. M. Vogel, S. Weber and C. Zirpins , Experiences on migrating restful web services to GraphQL, in Int. Conf. Service-Oriented Computing, 2017, pp. 283–295. Google Scholar
    • 3. S. K. Mukhiya, F. Rabbi, V. K. I. Pun, A. Rutle and Y. Lamo , A GraphQL approach to healthcare information exchange with HL7 FHIR, Proc. Comput. Sci. 160 (2019) 338–345. CrossrefGoogle Scholar
    • 4. Facebook, Inc., GraphQL, 2018, https://facebook.github.io/graphql/June2018/. Google Scholar
    • 5. S. Das, S. Sundara and R. Cyganiak, R2RML: RDB to RDF Mapping Language, 2018, https://www.w3.org/TR/r2rml/. Google Scholar
    • 6. A. Dimou, M. Vander Sande, P. Colpaert, R. Verborgh, E. Mannens and R. Van de Walle , RML: A generic language for integrated RDF mappings of heterogeneous data, in Proc. 7th Workshop on Linked Data on the Web, 2014, http://ceur-ws.org/Vol-1184/. Google Scholar
    • 7. A. Poggi, D. Lembo, D. Calvanese, G. De Giacomo, M. Lenzerini and R. Rosati, Linking data to ontologies, Journal on Data Semantics X (2008) 133–173. Google Scholar
    • 8. F. Michel, L. Djimenou, C. Faron-Zucker and J. Montagnat , Translation of relational and non-relational databases into RDF with xR2RML, in 11th Int. Conf. Web Information Systems and Technologies, 2015, pp. 443–454. CrossrefGoogle Scholar
    • 9. J. Slepicka, C. Yin, P. A. Szekely and C. A. Knoblock , KR2RML: An alternative interpretation of R2RML for heterogenous sources, in Proc. Consuming Linked Data, 2015, http://ceur-ws.org/Vol-1426/. Google Scholar
    • 10. D. Chaves-Fraga, F. Priyatna, I. Perez-Santana and O. Corcho , Virtual statistics knowledge graph generation from CSV files, in Emerging Topics in Semantic Technologies: ISWC 2018 Satellite Events, Studies on the Semantic Web, Vol. 36, 2018, pp. 235–244. Google Scholar
    • 11. P. Heyvaert, A. Dimou, A.-L. Herregodts, R. Verborgh, D. Schuurman, E. Mannens and R. Van de Walle , Rmleditor: A graph-based mapping editor for linked data mappings, in European Semantic Web Conference, 2016, pp. 709–723. CrossrefGoogle Scholar
    • 12. C. A. Knoblock and P. Szekely, Exploiting semantics for big data integration, AI Magazine 36(1) (2015) 25–38. Google Scholar
    • 13. A. Chebotko, S. Lu and F. Fotouhi , Semantics preserving SPARQL-to-SQL translation, Data Knowl. Eng. 68(10) (2009) 973–1000. Crossref, Web of ScienceGoogle Scholar
    • 14. F. Priyatna, D. Chaves-Fraga, A. Alobaid and O. Corcho , Morph-GraphQL: GraphQL servers generation from R2RML mappings, in Proc. 31st Int. Conf. Software Engineering and Knowledge Engineering, 2019. CrossrefGoogle Scholar
    • 15. O. Hartig, S. Cheng and L. Lindqvist, Linköping GraphQL Benchmark (LinGBM) kernel description, 2019, https://github.com/LiUGraphQL/LinGBM. Google Scholar
    • 16. C. Bizer and A. Schultz , The Berlin SPARQL benchmark, Int. J. Semantic Web Inf. Syst. 5(2) (2009) 1–24. Crossref, Web of ScienceGoogle Scholar
    • 17. F. Priyatna, O. Corcho and J. Sequeda , Formalisation and experiences of R2RML-based SPARQL to SQL query translation using Morph, in Proc. 23rd Int. Conf. World Wide Web, 2014, pp. 479–490. CrossrefGoogle Scholar
    • 18. K. M. Endris, P. D. Rohde, M.-E. Vidal and S. Auer , Ontario: Federated query processing against a semantic data lake, in Int. Conf. Database and Expert Systems Applications, 2019, pp. 379–395. CrossrefGoogle Scholar
    • 19. R. Taelman, M. Vander Sande and R. Verborgh , GraphQL-LD: Linked Data Querying with GraphQL, in 17th Int. Semantic Web Conference, 2018, http://ceur-ws.org/Vol-2180/. Google Scholar
    • 20. M. Sporny, D. Longley, G. Kellogg, M. Lanthaler and N. Lindström, JSON-LD 1.0, W3C Recommendation 16 (2014) 41. Google Scholar
    • 21. J. Werbrouck, M. Senthilvel, J. Beetz, P. Bourreau and L. Van Berlo , Semantic query languages for knowledge-based web services in a construction context, in Proc. 26th Int. Workshop on Intelligent Computing in Engineering, 2019, http://ceur-ws.org/Vol-2394/. Google Scholar
    • 22. D. Calvanese, B. Cogrel, S. Komla-Ebri, R. Kontchakov, D. Lanti, M. Rezk, M. Rodriguez-Muro and G. Xiao , Ontop: Answering SPARQL queries over relational databases, Semantic Web 8(3) (2017) 471–487. Crossref, Web of ScienceGoogle Scholar
    • 23. O. Hartig and J. Pérez , Semantics and complexity of GraphQL, in Proc. World Wide Web Conf., International World Wide Web Conferences Steering Committee, 2018, pp. 1155–1164. CrossrefGoogle Scholar
    • 24. O. Corcho, F. Priyatna and D. Chaves-Fraga , Towards a new generation of ontology based data access, Semantic Web J. 11(1) (2019) 153–160. Crossref, Web of ScienceGoogle Scholar
    • 25. A. Iglesias-Molina, D. Chaves-Fraga, F. Priyatna and O. Corcho , Towards the definition of a language-independent mapping template for knowledge graph creation, in Proc. Third Int. Workshop on Capturing Scientific Knowledge, 2019, http://ceur-ws.org/Vol-2526/. Google Scholar
    • 26. P. Heyvaert, B. De Meester, A. Dimou and R. Verborgh , Declarative rules for linked data generation at your fingertips!, in Proc. 15th ESWC: Posters and Demos, 2018. CrossrefGoogle Scholar
    • 27. D. Chaves-Fraga, E. Ruckhaus, F. Priyatna, M.-E. Vidal and O. Corcho, Enhancing OBDA query translation over tabular data with Morph-CSV, preprint, 2020, arXiv:2001.09052. Google Scholar
    • 28. L. F. de Medeiros, F. Priyatna and O. Corcho , Mirror: Automatic R2RML mapping generation from relational databases, in Int. Conf. Web Engineering, 2015, pp. 326–343. CrossrefGoogle Scholar
    • 29. Á. Sicilia and G. Nemirovski , Automap4obda: Automated generation of R2RML mappings for obda, in European Knowledge Acquisition Workshop, 2016, pp. 577–592. CrossrefGoogle Scholar
    • 30. E. Jiménez-Ruiz, E. Kharlamov, D. Zheleznyakov, I. Horrocks, C. Pinkel, M. G. Skjæveland, E. Thorstensen and J. Mora , BootOX: Practical mapping of RDBS to OWL 2, in Int. Semantic Web Conf., 2015, pp. 113–132. CrossrefGoogle Scholar
    • 31. F. Priyatna, E. Ruckhaus, N. Mihindukulasooriya, Ó. Corcho and N. Saturno , Mappingpedia: A collaborative environment for R2RML mappings, in European Semantic Web Conference, 2017, pp. 114–119. CrossrefGoogle Scholar
    • 32. A. Alobaid and O. Corcho , Fuzzy semantic labeling of semi-structured numerical datasets, in European Knowledge Acquisition Workshop, 2018, pp. 19–33. CrossrefGoogle Scholar
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