Towards Fuzzy Query-Relaxation for RDF

  • Aidan Hogan
  • Marc Mellotte
  • Gavin Powell
  • Dafni Stampouli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7295)


In this paper, we argue that query relaxation over RDF data is an important but largely overlooked research topic: the Semantic Web standards allow for answering crisp queries over crisp RDF data, but what of use-cases that require approximate answers for fuzzy queries over crisp data? We introduce a use-case from an EADS project that aims to aggregate intelligence information for police post-incident analysis. Query relaxation is needed to match incomplete descriptions of entities involved in crimes to structured descriptions thereof. We first discuss the use-case, formalise the problem, and survey current literature for possible approaches. We then present a proof-of-concept framework for enabling relaxation of structured entity-lookup queries, evaluating different distance measures for performing relaxation. We argue that beyond our specific scenario, query relaxation is important to many potential use-cases for Semantic Web technologies, and worthy of more attention.


Licence Plate Query Relaxation Fuzzy Query Concurrence Method Ford Focus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: DBpedia - a crystallization point for the Web of Data. J. Web Sem. 7(3), 154–165 (2009)CrossRefGoogle Scholar
  2. 2.
    Boriah, S., Chandola, V., Kumar, V.: Similarity measures for categorical data: A comparative evaluation. In: SDM, pp. 243–254 (2008)Google Scholar
  3. 3.
    Bruno, N., Chaudhuri, S., Gravano, L.: Top-k selection queries over relational databases: Mapping strategies and performance evaluation. ACM Trans. DB Syst. 27(2), 153–187 (2002)CrossRefGoogle Scholar
  4. 4.
    Chaudhuri, S., Datar, M., Narasayya, V.R.: Index selection for databases: A hardness study and a principled heuristic solution. IEEE Trans. Knowl. Data Eng. 16(11), 1313–1323 (2004)CrossRefGoogle Scholar
  5. 5.
    Chu, W.W.: Cooperative database systems. In: Wiley Encyclopedia of Computer Science and Engineering, John Wiley & Sons, Inc. (2008)Google Scholar
  6. 6.
    Dabrowski, M., Acton, T.: Modelling preference relaxation in e-commerce. In: FUZZ-IEEE, pp. 1–8 (2010)Google Scholar
  7. 7.
    Dolog, P., Stuckenschmidt, H., Wache, H., Diederich, J.: Relaxing RDF queries based on user and domain preferences. J. Intell. Inf. Syst. 33(3), 239–260 (2009)CrossRefGoogle Scholar
  8. 8.
    Elbassuoni, S., Ramanath, M., Weikum, G.: Query Relaxation for Entity-Relationship Search. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part II. LNCS, vol. 6644, pp. 62–76. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Gaasterland, T.: Cooperative answering through controlled query relaxation. IEEE Expert 12(5), 48–59 (1997)CrossRefGoogle Scholar
  10. 10.
    Gaasterland, T., Godfrey, P., Minker, J.: An overview of cooperative answering. J. Intell. Inf. Syst. 1(2), 123–157 (1992)CrossRefGoogle Scholar
  11. 11.
    Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In: IJCAI, pp. 1606–1611 (2007)Google Scholar
  12. 12.
    Goodall, D.W.: A new similarity index based on probability. Biometrics 22(4) (1966)Google Scholar
  13. 13.
    Grice, P.: Logic and conversation. Syntax and Semantics 3 (1975)Google Scholar
  14. 14.
    Hogan, A., Zimmermann, A., Umbrich, J., Polleres, A., Decker, S.: Scalable and distributed methods for entity matching, consolidation and disambiguation over Linked Data corpora. J. Web Sem. 10, 76–110 (2012)CrossRefGoogle Scholar
  15. 15.
    Hu, W., Chen, J., Qu, Y.: A self-training approach for resolving object coreference on the semantic web. In: WWW, pp. 87–96 (2011)Google Scholar
  16. 16.
    Huang, H., Liu, C., Zhou, X.: Computing Relaxed Answers on RDF Databases. In: Bailey, J., Maier, D., Schewe, K.-D., Thalheim, B., Wang, X.S. (eds.) WISE 2008. LNCS, vol. 5175, pp. 163–175. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Hurtado, C.A., Poulovassilis, A., Wood, P.T.: Query Relaxation in RDF. In: Spaccapietra, S. (ed.) Journal on Data Semantics X. LNCS, vol. 4900, pp. 31–61. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  18. 18.
    Ioannou, E., Papapetrou, O., Skoutas, D., Nejdl, W.: Efficient Semantic-Aware Detection of Near Duplicate Resources. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part II. LNCS, vol. 6089, pp. 136–150. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  19. 19.
    Kiefer, C., Bernstein, A., Stocker, M.: The Fundamentals of iSPARQL: A Virtual Triple Approach for Similarity-Based Semantic Web Tasks. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ISWC/ASWC 2007. LNCS, vol. 4825, pp. 295–309. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. 20.
    Lopes, N., Polleres, A., Straccia, U., Zimmermann, A.: AnQL: SPARQLing Up Annotated RDFS. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 518–533. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  21. 21.
    Maali, F., Cyganiak, R., Peristeras, V.: Re-using cool URIs: Entity reconciliation against LOD hubs. In: LDOW (2011)Google Scholar
  22. 22.
    Nikolov, A., Uren, V.S., Motta, E., De Roeck, A.: Integration of Semantically Annotated Data by the KnoFuss Architecture. In: Gangemi, A., Euzenat, J. (eds.) EKAW 2008. LNCS (LNAI), vol. 5268, pp. 265–274. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  23. 23.
    Noessner, J., Niepert, M., Meilicke, C., Stuckenschmidt, H.: Leveraging Terminological Structure for Object Reconciliation. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part II. LNCS, vol. 6089, pp. 334–348. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  24. 24.
    Oldakowski, R., Bizer, C.: SemMF: A framework for calculating semantic similarity of objects represented as RDF graphs. In: ISWC (Poster Proc.) (2005)Google Scholar
  25. 25.
    Poulovassilis, A., Wood, P.T.: Combining Approximation and Relaxation in Semantic Web Path Queries. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 631–646. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  26. 26.
    Saïs, F., Pernelle, N., Rousset, M.-C.: Combining a Logical and a Numerical Method for Data Reconciliation. In: Spaccapietra, S. (ed.) Journal on Data Semantics XII. LNCS, vol. 5480, pp. 66–94. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  27. 27.
    Schumacher, J., Bergmann, R.: An Efficient Approach to Similarity-Based Retrieval on Top of Relational Databases. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS (LNAI), vol. 1898, pp. 273–284. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  28. 28.
    Stampouli, D., Brown, M., Powell, G.: Fusion of soft information using TBM. In: 13th Int. Conf. on Information Fusion (2010)Google Scholar
  29. 29.
    Stampouli, D., Roberts, M., Powell, G.: Who dunnit? An appraisal of two people matching techniques. In: 14th Int. Conf. on Information Fusion (2011)Google Scholar
  30. 30.
    Stampouli, D., Vincen, D., Powell, G.: Situation assessment for a centralised intelligence fusion framework for emergency services. In: 12th Int. Conf. on Information Fusion (2009)Google Scholar
  31. 31.
    Stuckenschmidt, H.: A Semantic Similarity Measure for Ontology-Based Information. In: Andreasen, T., Yager, R.R., Bulskov, H., Christiansen, H., Larsen, H.L. (eds.) FQAS 2009. LNCS, vol. 5822, pp. 406–417. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  32. 32.
    Tkalčič, M., Tasič, J.F.: Colour spaces: perceptual, historical and applicational background. In: IEEE EUROCON, pp. 304–308 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Aidan Hogan
    • 1
  • Marc Mellotte
    • 1
  • Gavin Powell
    • 2
  • Dafni Stampouli
    • 2
  1. 1.Digital Enterprise Research Institute (DERI)National University of IrelandGalwayIreland
  2. 2.Innovation WorksEuropean Aeronautic Defence and Space Company (EADS)NewportUK

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