Predicting SPARQL Query Performance and Explaining Linked Data

  • Rakebul Hasan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8465)


As the complexity of the Semantic Web increases, efficient ways to query the Semantic Web data is becoming increasingly important. Moreover, consumers of the Semantic Web data may need explanations for debugging or understanding the reasoning behind producing the data. In this paper, firstly we address the problem of SPARQL query performance prediction. Secondly we discuss how to explain Linked Data in a decentralized fashion. Finally we discuss how to summarize the explanations.


#eswcphd2014Hasan query performance explanation summarization 


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  1. 1.
    RDF semantics. W3C recommendation (2004)Google Scholar
  2. 2.
    Akdere, M., Cetintemel, U., Riondato, M., Upfal, E., Zdonik, S.: Learning-based query performance modeling and prediction. In: 2012 IEEE 28th International Conference on Data Engineering (ICDE), pp. 390–401 (2012)Google Scholar
  3. 3.
    Altman, N.: An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician 46(3), 175–185 (1992)MathSciNetGoogle Scholar
  4. 4.
    Angele, J., Moench, E., Oppermann, H., Staab, S., Wenke, D.: Ontology-based query and answering in chemistry: Ontonova project halo. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 913–928. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Berners-Lee, T.: Linked Data. W3C Design Issues (2006),
  6. 6.
    Bizer, C.: Quality-Driven Information Filtering in the Context of Web-Based Information Systems. Ph.D. thesis, Freie Universität Berlin, Universitätsbibliothek (2007)Google Scholar
  7. 7.
    Bonatti, P.A., Hogan, A., Polleres, A., Sauro, L.: Robust and scalable linked data reasoning incorporating provenance and trust annotations. Web Semantics: Science, Services and Agents on the World Wide Web 9(2), 165–201 (2011)CrossRefGoogle Scholar
  8. 8.
    Carroll, J.J., Bizer, C., Hayes, P., Stickler, P.: Named graphs, provenance and trust. In: Proceedings of the 14th International Conference on World Wide Web, WWW 2005, pp. 613–622. ACM, New York (2005)CrossRefGoogle Scholar
  9. 9.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011),
  10. 10.
    Corby, O., Dieng-Kuntz, R., Gandon, F., Faron-Zucker, C.: Searching the Semantic Web: approximate query processing based on ontologies. IEEE Intelligent Systems 21(1), 20–27 (2006)CrossRefGoogle Scholar
  11. 11.
    Forcher, B., Sintek, M., Roth-Berghofer, T., Dengel, A.: Explanation-aware system design of the semantic search engine koios. In: Proc. of the the 5th Int’l. Workshop on Explanation-aware Computing (2010)Google Scholar
  12. 12.
    Frasincar, F., Houben, G.J., Vdovjak, R., Barna, P.: RAL: An algebra for querying RDF. World Wide Web 7(1), 83–109 (2004)CrossRefGoogle Scholar
  13. 13.
    Ganapathi, A., Kuno, H., Dayal, U., Wiener, J.L., Fox, A., Jordan, M., Patterson, D.: Predicting multiple metrics for queries: Better decisions enabled by machine learning. In: Proceedings of the 2009 IEEE International Conference on Data Engineering, ICDE 2009, pp. 592–603. IEEE Computer Society, Washington, DC (2009)Google Scholar
  14. 14.
    Gupta, C., Mehta, A., Dayal, U.: PQR: Predicting query execution times for autonomous workload management. In: Proceedings of the 2008 International Conference on Autonomic Computing, ICAC 2008, pp. 13–22. IEEE Computer Society, Washington, DC (2008)Google Scholar
  15. 15.
    Hartig, O., Heese, R.: The sparql query graph model for query optimization. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 564–578. Springer, Heidelberg (2007)Google Scholar
  16. 16.
    Hasan, R.: Generating and summarizing explanations for linked data. In: Proc. of the 11th Extended Semantic Web Conference (to appear 2014)Google Scholar
  17. 17.
    Heath, T., Bizer, C.: Linked Data: Evolving the Web into a Global Data Space, 1st edn. Morgan & Claypool (2011),
  18. 18.
    Huang, J., Abadi, D.J., Ren, K.: Scalable SPARQL querying of large RDF graphs. Proceedings of the VLDB Endowment 4(11), 1123–1134 (2011)Google Scholar
  19. 19.
    Kaufman, L., Rousseeuw, P.: Clustering by means of medoids. In: Dodge, Y. (ed.) Statistical Data Analysis based on the L1 Norm, pp. 405–416 (1987)Google Scholar
  20. 20.
    McGuinness, D., Furtado, V., da Pinheiro Silva, P., Ding, L., Glass, A., Chang, C.: Explaining semantic web applications. In: Semantic Web Engineering in the Knowledge Society (2008)Google Scholar
  21. 21.
    McGuinness, D., da Pinheiro Silva, P.: Explaining answers from the semantic web: the inference web approach. Web Semantics: Science, Services and Agents on the World Wide Web 1(4), 397–413 (2004)CrossRefGoogle Scholar
  22. 22.
    Morsey, M., Lehmann, J., Auer, S., Ngonga Ngomo, A.-C.: DBpedia SPARQL benchmark – performance assessment with real queries on real 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. 454–469. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  23. 23.
    Pelleg, D., Moore, A.W.: X-means: Extending K-means with efficient estimation of the number of clusters. In: Proceedings of the Seventeenth International Conference on Machine Learning, ICML 2000, pp. 727–734. Morgan Kaufmann, San Francisco (2000)Google Scholar
  24. 24.
    Riesen, K., Bunke, H.: Approximate graph edit distance computation by means of bipartite graph matching. Image Vision Comput. 27(7), 950–959 (2009)CrossRefGoogle Scholar
  25. 25.
    Shevade, S.K., Keerthi, S.S., Bhattacharyya, C., Murthy, K.R.K.: Improvements to the SMO algorithm for SVM regression. IEEE Transactions on Neural Networks 11(5), 1188–1193 (2000)CrossRefGoogle Scholar
  26. 26.
    da Pinheiro Silva, P., McGuinness, D., Fikes, R.: A proof markup language for semantic web services. Information Systems 31(4-5), 381–395 (2006)CrossRefGoogle Scholar
  27. 27.
    Stocker, M., Seaborne, A., Bernstein, A., Kiefer, C., Reynolds, D.: SPARQL basic graph pattern optimization using selectivity estimation. In: Proceedings of the 17th International Conference on World Wide Web, WWW 2008, pp. 595–604. ACM, New York (2008)Google Scholar
  28. 28.
    Tsialiamanis, P., Sidirourgos, L., Fundulaki, I., Christophides, V., Boncz, P.: Heuristics-based query optimisation for SPARQL. In: Proceedings of the 15th International Conference on Extending Database Technology, EDBT 2012, pp. 324–335. ACM, New York (2012)Google Scholar
  29. 29.
    Zhang, X., Cheng, G., Qu, Y.: Ontology summarization based on RDF sentence graph. In: Proceedings of the 16th International Conference on World Wide Web, WWW 2007, pp. 707–716. ACM, New York (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Rakebul Hasan
    • 1
  1. 1.INRIA Sophia AntipolisSophia-Antipolis CedexFrance

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