A SPARQL Query Transformation Rule Language — Application to Retrieval and Adaptation in Case-Based Reasoning

  • Olivier Bruneau
  • Emmanuelle Gaillard
  • Nicolas Lasolle
  • Jean Lieber
  • Emmanuel Nauer
  • Justine Reynaud
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10339)

Abstract

This paper presents SQTRL, a language for transformation rules for SPARQL queries, a tool associated with it, and how it can be applied to retrieval and adaptation in case-based reasoning (CBR). Three applications of SQTRL are presented in the domains of cooking and digital humanities. For a CBR system using RDFS for representing cases and domain knowledge, and SPARQL for its query language, case retrieval with SQTRL consists in a minimal modification of the query so that it matches at least a source case. Adaptation based on the modification of an RDFS base can also be handled with the help of this tool. SQTRL and its tool can therefore be used for several goals related to CBR systems based on the semantic web standards RDFS and SPARQL.

Keywords

RDFS SPARQL Query transformation Retrieval Adaptation Application 
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Notes

Acknowledgments

The authors would like to thank the anonymous reviewers for their comments that have helped to improve this paper.

References

  1. 1.
    Alchourrón, C.E., Gärdenfors, P., Makinson, D.: On the logic of theory change: partial meet functions for contraction and revision. J. Symb. Logic 50, 510–530 (1985)CrossRefMATHGoogle Scholar
  2. 2.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked data - the story so far. Semant. Serv. Interoperability Web Appl.: Emerg. Concepts, 205–227 (2009)Google Scholar
  3. 3.
    Brickley, D., Guha, R.V.: RDF Schema 1.1, W3C recommendation (2014). https://www.w3.org/TR/rdf-schema/. Last consultation March 2017
  4. 4.
    Bruneau, O., Garlatti, S., Guedj, M., Laubé, S., Lieber, J.: SemanticHPST: applying semantic web principles and technologies to the history and philosophy of science and technology. In: Gandon, F., Guéret, C., Villata, S., Breslin, J., Faron-Zucker, C., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9341, pp. 416–427. Springer, Cham (2015). doi: 10.1007/978-3-319-25639-9_53 CrossRefGoogle Scholar
  5. 5.
    Cojan, J., Lieber, J.: Applying belief revision to case-based reasoning. In: Prade, H., Richard, G. (eds.) Computational Approaches to Analogical Reasoning: Current Trends. SCI, vol. 548, pp. 133–161. Springer, Heidelberg (2014). doi: 10.1007/978-3-642-54516-0_6 CrossRefGoogle Scholar
  6. 6.
    Corby, O., Gaignard, A., Faron-Zucker, C., Montagnat, J.: KGRAM versatile data graphs querying and inference engine. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, Macau, December 2012Google Scholar
  7. 7.
    Cordier, A., et al.: Taaable: a case-based system for personalized cooking. In: Montani, S., Jain, L.C. (eds.) Successful Case-based Reasoning Applications-2. SCI, vol. 494, pp. 121–162. Springer, Heidelberg (2014). doi: 10.1007/978-3-642-38736-4_7 CrossRefGoogle Scholar
  8. 8.
    Cordier, A., Lieber, J., Molli, P., Nauer, E., Skaf-Molli, H., Toussaint, Y.: WikiTaaable: a semantic wiki as a blackboard for a textual case-based reasoning system. In: SemWiki 2009–4th Semantic Wiki Workshop, Heraklion, Greece, May 2009Google Scholar
  9. 9.
    Cox, M.T., Muñoz-Avila, H., Bergmann, R.: Case-based planning. Knowl. Eng. Rev. 20(3), 283–287 (2005)CrossRefGoogle Scholar
  10. 10.
    Dietterich, T.G., Michalski, R.S.: A comparative review of selected methods for learning from examples. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) Machine Learning, pp. 41–81. Springer, Heidelberg (1983). doi: 10.1007/978-3-662-12405-5_3 Google Scholar
  11. 11.
    Gaillard, E., Infante-Blanco, L., Lieber, J., Nauer, E.: Tuuurbine: a generic CBR engine over RDFS. In: Lamontagne, L., Plaza, E. (eds.) ICCBR 2014. LNCS, vol. 8765, pp. 140–154. Springer, Cham (2014). doi: 10.1007/978-3-319-11209-1_11 Google Scholar
  12. 12.
    Kiani, N., Lieber, J., Nauer, E., Schneider, J.: Analogical transfer in RDFS, application to cocktail name adaptation. In: Goel, A., Díaz-Agudo, M.B., Roth-Berghofer, T. (eds.) ICCBR 2016. LNCS, vol. 9969, pp. 218–233. Springer, Cham (2016). doi: 10.1007/978-3-319-47096-2_15 CrossRefGoogle Scholar
  13. 13.
    Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, Inc., Burlington (1993)CrossRefMATHGoogle Scholar
  14. 14.
    Lieber, J., Napoli, A.: Using classification in case-based planning. In: Wahlster, W., (ed.) Proceedings of the 12th European Conference on Artificial Intelligence (ECAI 1996), pp. 132–136. Wiley, Budapest (1996)Google Scholar
  15. 15.
    Minor, M., Bergmann, R., Görg, S.: Case-based adaptation of workflows. Inf. Syst. 40, 142–152 (2014)CrossRefGoogle Scholar
  16. 16.
    Ontañón, S., Shokoufandeh, A.: Refinement-based similarity measures for directed labeled graphs. In: Goel, A., Díaz-Agudo, M.B., Roth-Berghofer, T. (eds.) ICCBR 2016. LNCS, vol. 9969, pp. 311–326. Springer, Cham (2016). doi: 10.1007/978-3-319-47096-2_21 CrossRefGoogle Scholar
  17. 17.
    Ontañón, S., Plaza, E.: Amalgams: a formal approach for combining multiple case solutions. In: Bichindaritz, I., Montani, S. (eds.) ICCBR 2010. LNCS, vol. 6176, pp. 257–271. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-14274-1_20 CrossRefGoogle Scholar
  18. 18.
    Recio-Garía, J.A., Sánchez, A., Díaz-Agudo, B., González-Calero, P.A.: JColibri 1.0 in a nutshell. A software tool for designing CBR systems. In: Petridis, M., (ed.) Proceedings of the 10th UK Workshop on Case Based Reasoning, pp. 20–28. CMS Press, University of Greenwich (2005)Google Scholar
  19. 19.
    Riesbeck, C.K., Schank, R.C.: Inside Case-Based Reasoning. Lawrence Erlbaum Associates Inc., Hillsdale (1989)Google Scholar
  20. 20.
    Shimazu, H., Kitano, H., Shibata, A.: Retrieving cases from relational data-bases: another stride towards corporate-wide case-based systems. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence (IJCAI 1993), Chambéry, pp. 909–914 (1993)Google Scholar
  21. 21.
    Watson, I.: Is CBR a technology or a methodology? In: Pasqual del Pobil, A., Mira, J., Ali, M. (eds.) IEA/AIE 1998. LNCS, vol. 1416, pp. 525–534. Springer, Heidelberg (1998). doi: 10.1007/3-540-64574-8_438 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Olivier Bruneau
    • 1
  • Emmanuelle Gaillard
    • 2
  • Nicolas Lasolle
    • 1
  • Jean Lieber
    • 2
  • Emmanuel Nauer
    • 2
  • Justine Reynaud
    • 2
  1. 1.University of Lorraine, LHSP-AHPNancy CedexFrance
  2. 2.UL, CNRS, Inria, LoriaNancyFrance

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