FedSDM: Semantic Data Manager for Federations of RDF Datasets

  • Kemele M. EndrisEmail author
  • Maria-Esther Vidal
  • Sören Auer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11371)


Linked open data movements have been followed successfully in different domains; thus, the number of publicly available RDF datasets and linked data based applications have increased considerably during the last decade. Particularly in Life Sciences, RDF datasets are utilized to represent diverse concepts, e.g., proteins, genes, mutations, diseases, drugs, and side effects. Albeit publicly accessible, the exploration of these RDF datasets requires the understanding of their main characteristics, e.g., their vocabularies and the connections among them. To tackle these issues, we present and demonstrate FedSDM, a semantic data manager for federations of RDF datasets. Attendees will be able to explore the relationships among the RDF datasets in a federation, as well as the characteristics of the RDF classes included in each RDF dataset (



This work has been funded by the EU H2020 RIA under the Marie Skłodowska-Curie grant agreement No. 642795 (WDAqua) and EU H2020 Programme for the projects with GA No. 727658 (IASIS).


  1. 1.
    Acosta, M., Vidal, M.-E., Lampo, T., Castillo, J., Ruckhaus, E.: ANAPSID: An adaptive query processing engine for SPARQL endpoints. In: Aroyo, L., et al. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 18–34. Springer, Heidelberg (2011). Scholar
  2. 2.
    Beek, W., Fernández, J.D., Verborgh, R.: LOD-a-lot: a single-file enabler for data science. In: Proceedings of the 13th International Conference on Semantic Systems, SEMANTICS 2017, Amsterdam, The Netherlands, 11–14 Sept 2017, pp. 181–184 (2017)Google Scholar
  3. 3.
    Charalambidis, A., Troumpoukis, A., Konstantopoulos, S.: SemaGrow: optimizing federated sparql queries. In: Proceedings of the 11th International Conference on Semantic Systems, pp. 121–128. ACM (2015)Google Scholar
  4. 4.
    Endris, K.M., Almhithawi, Z., Lytra, I., Vidal, M.-E., Auer, S.: BOUNCER: privacy-aware query processing over federations of rdf datasets. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R.R. (eds.) DEXA 2018. LNCS, vol. 11029, pp. 69–84. Springer, Cham (2018). Scholar
  5. 5.
    Endris, K.M., Galkin, M., Lytra, I., Mami, M.N., Vidal, M.-E., Auer, S.: MULDER: querying the linked data web by bridging rdf molecule templates. In: Benslimane, D., Damiani, E., Grosky, W.I., Hameurlain, A., Sheth, A., Wagner, R.R. (eds.) DEXA 2017. LNCS, vol. 10438, pp. 3–18. Springer, Cham (2017). Scholar
  6. 6.
    Fundulaki, I., Auer, S.: Linked open data - introduction to the special theme. ERCIM News 2014(96) (2014)Google Scholar
  7. 7.
    Görlitz, O., Staab, S.: SPLENDID: SPARQL endpoint federation exploiting VOID descriptions. In: COLD (2011)Google Scholar
  8. 8.
    Schwarte, A., Haase, P., Hose, K., Schenkel, R., Schmidt, M.: FedX: optimization techniques for federated query processing on linked data. In: Aroyo, L., et al. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 601–616. Springer, Heidelberg (2011). Scholar
  9. 9.
    Vidal, M., Castillo, S., Acosta, M., Montoya, G., Palma, G.: On the selection of SPARQL endpoints to efficiently execute federated SPARQL queries. Trans. Large-Scale Data-Knowl.-Centered Syst. 25, 109–149 (2016)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.L3S Research CenterHannoverGermany
  2. 2.TIB Leibniz Information Centre for Science and TechnologyHannoverGermany

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