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SPARQ\(\lambda \): SPARQL as a Function

  • Christian Vogelgesang
  • Torsten SpieldennerEmail author
  • René Schubotz
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 69)

Abstract

With more and more applications providing semantic data to improve interoperability, the amount of available RDF datasets is constantly increasing. The SPARQL query language is a W3C recommendation to provide query capabilities on such RDF datasets. Yet as the coverage of RDF datasets with efficient and available SPARQL endpoints is still limited, integration of data from different RDF sources is a bottleneck that has mostly to be done in RDF consuming clients. We tackle this bottleneck by introducing SPARQ\(\lambda \), an extension to the SPARQL 1.1 query language. SPARQ\(\lambda \) enables dynamic injection of RDF datasets during evaluation of the query, and by this lifts SPARQL to a tool to write templates for RDF producing functions in functional programming style. This is an important step to reduce the effort to write SPARQL queries that work on data from various sources. SPARQ\(\lambda \) is moreover suitable to directly translate to an RDF described Web service interface, which allows to lift integration of data and re-provisioning of integrated results from clients to cloud environments.

Keywords

SPARQL Data integration RDF Functional programming 

Notes

Acknowledgment

This work is supported by the Federal Ministry of Education and Research of Germany in the project Hybr-iT (Förderkennzeichen 01IS16026A).

References

  1. 1.
  2. 2.
    Shape Expressions Language 2.0. https://www.w3.org/TR/shex-semantics/
  3. 3.
    Shapes Constraint Language (SHACL). https://www.w3.org/TR/shacl/
  4. 4.
  5. 5.
  6. 6.
    Abdelaziz, I., Harbi, R., Khayyat, Z., Kalnis, P.: A survey and experimental comparison of distributed SPARQL engines for very large RDF data. Proc. VLDB Endow. 10(13), 2049–2060 (2017)CrossRefGoogle Scholar
  7. 7.
    Baldini, I., Castro, P., Chang, K., Cheng, P., Fink, S., Ishakian, V., Mitchell, N., Muthusamy, V., Rabbah, R., Slominski, A., et al.: Serverless computing: current trends and open problems. In: Research Advances in Cloud Computing, pp. 1–20. Springer (2017)Google Scholar
  8. 8.
    Barbieri, D.F., Braga, D., Ceri, S., Valle, E.D., Grossniklaus, M.: C-SPARQL: a continuous query language for RDF data streams. Int. J. Semant. Comput. 4(01), 3–25 (2010)CrossRefGoogle Scholar
  9. 9.
    Buil-Aranda, C., Arenas, M., Corcho, O., Polleres, A.: Federating queries in SPARQL 1.1: syntax, semantics and evaluation. Web Semant. Sci. Serv. Agents World Wide Web 18(1), 1–17 (2013). Special Section on the Semantic and Social WebCrossRefGoogle Scholar
  10. 10.
    Buil-Aranda, C., Hogan, A., Umbrich, J., Vandenbussche, P.Y.: SPARQL web-querying infrastructure: ready for action? In: International Semantic Web Conference, pp. 277–293. Springer (2013)Google Scholar
  11. 11.
    Daga, E., Panziera, L., Pedrinaci, C.: A BASILar approach for building web APIs on top of SPARQL endpoints. In: CEUR Workshop Proceedings, vol. 1359, pp. 22–32 (2015)Google Scholar
  12. 12.
    Dia, A.F., Kazi-Aoul, Z., Boly, A., Chabchoub, Y.: C-SPARQL extension for sampling RDF graphs streams. In: Advances in Knowledge Discovery and Management, pp. 23–40. Springer (2018)Google Scholar
  13. 13.
    Fafalios, P., Tzitzikas, Y.: SPARQL-LD: a SPARQL extension for fetching and querying linked data. In: International Semantic Web Conference (Posters and Demos) (2015)Google Scholar
  14. 14.
    Fafalios, P., Yannakis, T., Tzitzikas, Y.: Querying the web of data with SPARQL-LD. In: International Conference on Theory and Practice of Digital Libraries, pp. 175–187. Springer (2016)Google Scholar
  15. 15.
    Fox, G.C., Ishakian, V., Muthusamy, V., Slominski, A.: Status of serverless computing and function-as-a-service (FaaS) in industry and research. arXiv preprint arXiv:1708.08028 (2017)
  16. 16.
    Jones, N.D., Gomard, C.K., Sestoft, P.: Partial Evaluation and Automatic Program Generation. Prentice-Hall International Series in Computer Science. Prentice-Hall, New York (1993)zbMATHGoogle Scholar
  17. 17.
    Leng, Y., Zhikui, C., Zhong, F., Li, X., Hu, Y., Yang, C.: BRGP: a balanced RDF graph partitioning algorithm for cloud storage. Concurr. Comput. Pract. Exp. 29(14), e3896 (2017)CrossRefGoogle Scholar
  18. 18.
    Michel, F., Faron-Zucker, C., Gandon, F.: Bridging web APIs and linked data with SPARQL micro-services. In: Extended Semantic Web Conference (ESWC) (2018)Google Scholar
  19. 19.
    Michel, F., Zucker, C.F., Gandon, F.: SPARQL micro-services: lightweight integration of web APIs and linked data. In: LDOW 2018-Linked Data on the Web, pp. 1–10 (2018)Google Scholar
  20. 20.
    Millard, I., Glaser, H., Salvadores, M., Shadbolt, N.: Consuming multiple linked data sources: challenges and experiences (2010)Google Scholar
  21. 21.
    Rakhmawati, N.A., Umbrich, J., Karnstedt, M., Hasnain, A., Hausenblas, M.: A comparison of federation over SPARQL endpoints frameworks. In: International Conference on Knowledge Engineering and the Semantic Web, pp. 132–146. Springer (2013)Google Scholar
  22. 22.
    Rietveld, L., Verborgh, R., Beek, W., Vander Sande, M., Schlobach, S.: Linked data-as-a-service: the semantic web redeployed. In: European Semantic Web Conference, pp. 471–487. Springer (2015)Google Scholar
  23. 23.
    Stadtmüller, S., Speiser, S., Harth, A.: Future challenges for linked APIs. In: SALAD@ ESWC, pp. 20–27 (2013)Google Scholar
  24. 24.
    Verborgh, R., Vander Sande, M., Colpaert, P., Coppens, S., Mannens, E., Van de Walle, R.: Web-scale querying through linked data fragments. In: LDOW. Citeseer (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Christian Vogelgesang
    • 1
  • Torsten Spieldenner
    • 1
    Email author
  • René Schubotz
    • 1
  1. 1.German Research Center for Artifical Intelligence (DFKI)Saarbrücken Graduate School of Computer ScienceSaarbrückenGermany

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