Ranking Approximate Answers to Semantic Web Queries

  • Carlos A. Hurtado
  • Alexandra Poulovassilis
  • Peter T. Wood
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5554)


We consider the problem of a user querying semistructured data such as RDF without knowing its structure. In these circumstances, it is helpful if the querying system can perform an approximate matching of the user’s query to the data and can rank the answers in terms of how closely they match the original query. Our approximate matching framework allows us to incorporate standard notions of approximation such as edit distance as well as certain RDFS inference rules, thereby capturing semantic as well as syntactic approximations. The query language we adopt comprises conjunctions of regular path queries, thus including extensions proposed for SPARQL to allow for querying paths using regular expressions. We provide an incremental query evaluation algorithm which runs in polynomial time and returns answers to the user in ranked order.


Regular Expression Edit Distance Edge Label Edit Operation Conjunctive Query 
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.
    Berners-Lee, T., Chen, Y., Chilton, L., Connolly, D., Dhanaraj, R., Hollenbach, J., Lerer, A., Sheets, D.: Tabulator: Exploring and analyzing linked data on the semantic web. In: Proc. 3rd Int. Semantic Web User Interaction Workshop (2006)Google Scholar
  2. 2.
    Heath, T., Hausenblas, M., Bizer, C., Cyganiak, R.: How to publish linked data on the web (tutorial). In: Proc. 7th Int. Semantic Web Conf. (2008)Google Scholar
  3. 3.
    Calvanese, D., Giacomo, G.D., Lenzerini, M., Vardi, M.Y.: Containment of conjunctive regular path queries with inverse. In: Proc. Seventh Int. Conf. on Principles of Knowledge Representation and Reasoning, pp. 176–185 (2000)Google Scholar
  4. 4.
    Cruz, I.F., Mendelzon, A.O., Wood, P.T.: A graphical query language supporting recursion. In: Proc. ACM SIGMOD Conf., pp. 323–330 (1987)Google Scholar
  5. 5.
    Kochut, K., Janik, M.: SPARQLeR: Extended SPARQL for semantic association discovery. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 145–159. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Pérez, J., Arenas, M., Gutierrez, C.: nSPARQL: A navigational language for RDF. In: Sheth, A., et al. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 66–81. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Prud’hommeaux, E., Seaborne, A. (eds.): SPARQL Query Language for RDF. W3C Candidate Recommendation (April 6, 2006)Google Scholar
  8. 8.
    Dolog, P., Stuckenschmidt, H., Wache, H.: Robust query processing for personalized information access on the semantic web. In: Larsen, H.L., Pasi, G., Ortiz-Arroyo, D., Andreasen, T., Christiansen, H. (eds.) FQAS 2006. LNCS, vol. 4027, pp. 343–355. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Grahne, G., Thomo, A.: Approximate reasoning in semi-structured databases. In: Proc. 8th Int. Workshop on Knowledge Representation meets Databases (2001)Google Scholar
  10. 10.
    Hurtado, C.A., Poulovassilis, A., Wood, P.T.: Query relaxation in RDF. Journal on Data Semantics X, 31–61 (2008)zbMATHGoogle Scholar
  11. 11.
    Jagadish, H.V., Mendelzon, A.O., Milo, T.: Similarity-based queries. In: Proc. Fourteenth ACM Symp. on Principles of Databases Systems, pp. 36–45 (1995)Google Scholar
  12. 12.
    Kanza, Y., Sagiv, Y.: Flexible queries over semistructured data. In: Proc. Twentieth ACM Symp. on Principles of Databases Systems, pp. 40–51 (2001)Google Scholar
  13. 13.
    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., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 295–309. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Ilyas, I.F., Aref, W.G., Elmagarmid, A.K.: Supporting top-k join queries in relational databases. The VLDB Journal 13, 207–221 (2004)CrossRefGoogle Scholar
  15. 15.
    Wu, S., Manber, U.: Fast text searching allowing errors. Commun. ACM 35(10), 83–91 (1992)CrossRefGoogle Scholar
  16. 16.
    Gottlob, G., Leone, N., Scarcello, F.: The complexity of acyclic conjunctive queries. J. ACM 43(3), 431–498 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Siberski, W., Pan, J.Z., Thaden, U.: Querying the semantic web with preferences. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 612–624. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    Bhalotia, G., Hulgeri, A., Nakhe, C., Chakrabarti, S., Sudarshan, S.: Keyword searching and browsing in databases using BANKS. In: Proc. 18th Int. Conf. on Data Engineering, pp. 431–440 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Carlos A. Hurtado
    • 1
  • Alexandra Poulovassilis
    • 2
  • Peter T. Wood
    • 2
  1. 1.Faculty of Engineering and SciencesUniversidad Adolfo IbáñezChile
  2. 2.School of Computer Science and Information SystemsBirkbeck, University of LondonUK

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