skip to main content
article

A probabilistic optimization framework for the empty-answer problem

Published:01 September 2013Publication History
Skip Abstract Section

Abstract

We propose a principled optimization-based interactive query relaxation framework for queries that return no answers. Given an initial query that returns an empty answer set, our framework dynamically computes and suggests alternative queries with less conditions than those the user has initially requested, in order to help the user arrive at a query with a non-empty answer, or at a query for which no matter how many additional conditions are ignored, the answer will still be empty. Our proposed approach for suggesting query relaxations is driven by a novel probabilistic framework based on optimizing a wide variety of application-dependent objective functions. We describe optimal and approximate solutions of different optimization problems using the framework. We analyze these solutions, experimentally verify their efficiency and effectiveness, and illustrate their advantage over the existing approaches.

References

  1. S. Agrawal, S. Chaudhuri, G. Das, and A. Gionis. Automated ranking of database query results. In CIDR, 2003.Google ScholarGoogle Scholar
  2. A. Anagnostopoulos, L. Becchetti, C. Castillo, and A. Gionis. An optimization framework for query recommendation. In WSDM, pages 161-170, 2010. Google ScholarGoogle Scholar
  3. B. Arai, G. Das, D. Gunopulos, and N. Koudas. Anytime measures for top-k algorithms on exact and fuzzy data sets. VLDB J., 18(2):407-427, 2009. Google ScholarGoogle Scholar
  4. R. A. Baeza-Yates, C. A. Hurtado, and M. Mendoza. Query recommendation using query logs in search engines. In EDBT Workshops, pages 588-596, 2004. Google ScholarGoogle Scholar
  5. R. A. Baeza-Yates and B. A. Ribeiro-Neto. Modern Information Retrieval. Addison-Wesley New York, 2011. Google ScholarGoogle Scholar
  6. S. Basu Roy, H. Wang, G. Das, U. Nambiar, and M. Mohania. Minimum-effort driven dynamic faceted search in structured databases. In CIKM, pages 13-22, 2008. Google ScholarGoogle Scholar
  7. Y. M. M. Bishop, S. E. Fienberg, and P. W. Holland. Discr. Multivariate Analysis: Theory and Practice. MIT Press, 1975.Google ScholarGoogle Scholar
  8. A. Chapman and H. V. Jagadish. Why not? In SIGMOD, pages 523-534, 2009. Google ScholarGoogle Scholar
  9. S. Chaudhuri. Generalization and a framework for query modification. In ICDE, pages 138-145, 1990. Google ScholarGoogle Scholar
  10. S. Chaudhuri, G. Das, V. Hristidis, and G. Weikum. Probabilistic ranking of database query results. In VLDB, pages 888-899, 2004. Google ScholarGoogle Scholar
  11. S. Chaudhuri, G. Das, V. Hristidis, and G. Weikum. Probabilistic information retrieval approach for ranking of database query results. ACM Trans. Database Syst., 31(3):1134-1168, 2006. Google ScholarGoogle Scholar
  12. S. Gauch and J. Smith. Search improvement via automatic query reformulation. TOIS, 9(3):249-280, 1991. Google ScholarGoogle Scholar
  13. S. Gauch and J. B. Smith. An expert system for automatic query reformulation. JASIS, 44(3):124-136, 1993.Google ScholarGoogle Scholar
  14. V. Hristidis, Y. Hu, and P. G. Ipeirotis. Ranked queries over sources with boolean query interfaces without ranking support. In ICDE, pages 872-875, 2010.Google ScholarGoogle Scholar
  15. D. Jannach. Techniques for fast query relaxation in content-based recommender systems. KI'06: Advances in AI, pages 49-63, 2007. Google ScholarGoogle Scholar
  16. D. Jannach and J. Liegl. Conflict-directed relaxation of constraints in content-based recommender systems. Advances in Applied AI, pages 819-829, 2006. Google ScholarGoogle Scholar
  17. U. Junker. Quickxplain: Preferred explanations and relaxations for over-constrained problems. In AAAI, volume 4, pages 167-172, 2004. Google ScholarGoogle Scholar
  18. A. Kashyap, V. Hristidis, and M. Petropoulos. Facetor: cost-driven exploration of faceted query results. In CIKM, pages 719-728, 2010. Google ScholarGoogle Scholar
  19. N. Koudas, C. Li, A. K. H. Tung, and R. Vernica. Relaxing join and selection queries. In VLDB, pages 199-210, 2006. Google ScholarGoogle Scholar
  20. C. Li, N. Yan, S. B. Roy, L. Lisham, and G. Das. Facetedpedia: dynamic generation of query-dependent faceted interfaces for wikipedia. In WWW, pages 651-660, 2010. Google ScholarGoogle Scholar
  21. D. McSherry. Incremental relaxation of unsuccessful queries. In ECCBR, pages 331-345, 2004.Google ScholarGoogle Scholar
  22. C. Mishra and N. Koudas. Interactive query refinement. In EDBT, pages 862-873. ACM, 2009. Google ScholarGoogle Scholar
  23. T. M. Mitchell. Machine Learning. McGraw-Hill, 1997. Google ScholarGoogle Scholar
  24. Q. T. Tran and C.-Y. Chan. How to conquer why-not questions. In SIGMOD, pages 15-26, 2010. Google ScholarGoogle Scholar
  25. J.-R. Wen, J.-Y. Nie, and H. Zhang. Query clustering using user logs. ACM Trans. Inf. Syst., 20(1):59-81, 2002. Google ScholarGoogle Scholar

Index Terms

  1. A probabilistic optimization framework for the empty-answer problem
      Index terms have been assigned to the content through auto-classification.

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      • Published in

        cover image Proceedings of the VLDB Endowment
        Proceedings of the VLDB Endowment  Volume 6, Issue 14
        September 2013
        384 pages

        Publisher

        VLDB Endowment

        Publication History

        • Published: 1 September 2013
        Published in pvldb Volume 6, Issue 14

        Qualifiers

        • article

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader