Federated Query Evaluation Supported by SPARQL Recommendation

  • Gergő GombosEmail author
  • Attila Kiss
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9734)


The usage of the semantic data is complicated for non-expert users, because the data are on many different SPARQL endpoints and the users need to know the URL of the endpoints. The federated systems are good solution for this problem. These systems interpret the SPARQL query and find the endpoints that can answer the query. Another advantage of the federated systems is it can connect the datasets with one simple query. This means some part of the data is found on different endpoints and the system can connect them. In order to do this the system needs information about the datasets that are stored by endpoints. We know two types for this. The first uses a catalog and stores the information in it. The system makes this information up-to-date at times. The second solution uses ASK query to decide which endpoint can answer the query. The queries run on every endpoint on every time. In this paper, we improve these federated systems. The basic idea is to reduce the number of the available endpoints. Our solution uses the SPARQL recommendation technique. This technique offers triple patterns to the user when the user writes the query. When the system asks new recommendation from the endpoints, it gets some information about the endpoints. This information is useful for the federated systems. It is enough to use only these endpoints given by the recommendations.

In this paper, we extend the recommendation technique with new recommendation type that is based on the rdfs:range predicate. We present a query cost model that represents the number of the queries that need to the federated query evaluation. We present the recommendation information is enough for the evaluation and we make experiments on two federated systems (FedX, DarQ).


SPARQL Federated Recommendation LOD Cloud 



Authors thank Ericsson Ltd. for support via the ELTE CNL collaboration.


  1. 1.
    Buil-Aranda, C., Polleres, A., Umbrich, J.: Strategies for executing federated queries in SPARQL1.1. In: Mika, P., et al. (eds.) ISWC 2014, Part II. LNCS, vol. 8797, pp. 390–405. Springer, Heidelberg (2014)Google Scholar
  2. 2.
    Campinas, S.: Live SPARQL auto-completion. In: ISWC 2014 Posters and Demonstrations Track, pp. 477–480 (2014).
  3. 3.
    Erling, O., Mikhailov, I.: RDF support in the virtuoso DBMS. In: Pellegrini, T., Auer, S., Tochtermann, K., Schaffert, S. (eds.) Networked Knowledge-Networked Media. SCI, vol. 221, pp. 7–24. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Gombos, G., Kiss, A.: SPARQL query writing with recommendations based on datasets. In: Yamamoto, S. (ed.) HCI 2014, Part I. LNCS, vol. 8521, pp. 310–319. Springer, Heidelberg (2014)Google Scholar
  5. 5.
    Hoefler, P.: Linked data interfaces for non-expert users. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 702–706. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Jena, A.: Semantic web framework for Java (2007)Google Scholar
  7. 7.
    Kramer, K., Dividino, R.Q., Gröner, G.: Space: SPARQL index for efficient autocompletion. In: International Semantic Web Conference (Posters and Demos), pp. 157–160 (2013)Google Scholar
  8. 8.
    Lehmann, J., Bühmann, L.: AutoSPARQL: let users query your knowledge base. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part I. LNCS, vol. 6643, pp. 63–79. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Quilitz, B.: DARQ-federated queries with SPARQL (2006)Google Scholar
  10. 10.
    Rakhmawati, N.A., Umbrich, J., Karnstedt, M., Hasnain, A., Hausenblas, M.: Querying over federated SPARQL endpoints–a state of the art survey (2013). arXiv preprint arXiv:1306.1723
  11. 11.
    Rietveld, L., Hoekstra, R.: YASGUI: not just another SPARQL client. In: Cimiano, P., Fernández, M., Lopez, V., Schlobach, S., Völker, J. (eds.) ESWC 2013. LNCS, vol. 7955, pp. 78–86. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  12. 12.
    Saleem, M., Ngonga Ngomo, A.-C.: HiBISCuS: hypergraph-based source selection for SPARQL endpoint federation. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 176–191. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  13. 13.
    Schmidt, M., Görlitz, O., Haase, P., Ladwig, G., Schwarte, A., Tran, T.: FedBench: a benchmark suite for federated semantic data query processing. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 585–600. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Schwarte, A., Haase, P., Hose, K., Schenkel, R., Schmidt, M.: FedX: optimization techniques for federated query processing on linked data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 601–616. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  15. 15.
    Verborgh, R., et al.: Querying datasets on the web with high availability. In: Mika, P., et al. (eds.) ISWC 2014, Part I. LNCS, vol. 8796, pp. 180–196. Springer, Heidelberg (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Eötvös Loránd UniversityBudapestHungary
  2. 2.J. Selye UniversityKomárnoSlovakia

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