World Wide Web

, Volume 22, Issue 4, pp 1727–1750 | Cite as

XPloreRank: exploring XML data via you may also like queries

  • Mehdi NaseriparsaEmail author
  • Chengfei Liu
  • Md. Saiful Islam
  • Rui Zhou


In many cases, users are not familiar with their exact information needs while searching complicated data sources. This lack of understanding may cause the users to feel dissatisfaction when the system retrieves insufficient results after they issue queries. However, using their original query results, we may recommend additional queries which are highly relevant to the original query. This paper presents XPloreRank to recommend top-l highly relevant keyword queries called “You May Also Like” (YMAL) queries to the users in XML keyword search. To generate such queries, we firstly analyze the original keyword query results content and construct a weighted co-occurring keyword graph. Then, we generate the YMAL queries by traversing the co-occurring keyword graph and rank them based on the following correlation aspects: (a) external correlation, which measures the similarity of the YMAL query to the original query and (b) internal correlation, which measures the capability of the YMAL query keywords in producing meaningful results with respect to the data source. Due to the complexity of generating YMAL queries, we propose a novel A* search-based technique to generate top-l YMAL queries efficiently. We also present a greedy-based approximation for it to improve the performance further. Extensive experiments verify the effectiveness and efficiency of our approach.


XML keyword search Data exploration Recommendations 



This work is supported by the Australian Research Council Discovery Grants DP140103499, DPDP170104747, and DP180100212.


  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Akbarnejad, J., Chatzopoulou, G., Eirinaki, M., Koshy, S., Mittal, S., On, D., Polyzotis, N., Varman, J.S.V.: SQL Querie recommendations. PVLDB 3(2), 1597–1600 (2010)Google Scholar
  3. 3.
    Baid, A., Wu, W., Sun, C., Doan, A., Naughton, J.F.: On debugging non-answers in keyword search systems. In: EDBT, pp 37–48 (2015)Google Scholar
  4. 4.
    Bao, Z., Ling, T. W., Chen, B., Lu, J.: Effective Xml keyword search with relevance oriented ranking. In: ICDE, pp 517–528 (2009)Google Scholar
  5. 5.
    Bao, Z., Zeng, Y., Ling, T.W., Zhang, D., Li, G., Jagadish, H.V.: A general framework to resolve the mismatch problem in XML keyword search. VLDB J. 24(4), 493–518 (2015)CrossRefGoogle Scholar
  6. 6.
    Cilibrasi, R., Vitányi, P.M.B.: The google similarity distance. IEEE Trans. Knowl. Data Eng. 19(3), 370–383 (2007)CrossRefGoogle Scholar
  7. 7.
    Cohen, S., Brodianskiy, T.: Correcting queries for XML. Inf. Syst. 34(8), 690–710 (2009)CrossRefGoogle Scholar
  8. 8.
    Drosou, M., Pitoura, E.: Ymaldb: exploring relational databases via result-driven recommendations. VLDB J. 22(6), 849–874 (2013)CrossRefGoogle Scholar
  9. 9.
    Ehsan, H., Sharaf, M.A., Chrysanthis, P.K.: Muve: efficient multi-objective view recommendation for visual data exploration. In: ICDE, pp 731–742 (2016)Google Scholar
  10. 10.
    Ge, X., Xue, Y., Luo, Z., Sharaf, M.A., Chrysanthis, P.K.: REQUEST: a scalable framework for interactive construction of exploratory queries. In: IEEE Bigdata, pp 646–655 (2016)Google Scholar
  11. 11.
    Guo, L., Shao, F., Botev, C., Shanmugasundaram, J.: Xrank: ranked keyword search over xml documents. In: SIGMOD, pp 16–27 (2003)Google Scholar
  12. 12.
    Huang, H., Chen, Z., Liu, C., Huang, H., Zhang, X.: An effective suggestion method for keyword search of databases. World Wide Web 20(4), 729–747 (2017)CrossRefGoogle Scholar
  13. 13.
    Islam, M.S., Liu, C., Li, J.: Efficient answering of why-not questions in similar graph matching. IEEE Trans. Knowl Data Eng. 27(10), 2672–2686 (2015)CrossRefGoogle Scholar
  14. 14.
    Islam, M.S., Liu, C., Zhou, R.: Flexiq: a flexible interactive querying framework by exploiting the skyline operator. J. Syst. Softw. 97, 97–117 (2014)CrossRefGoogle Scholar
  15. 15.
    Islam, M.S., Zhou, R., Liu, C.: On answering why-not questions in reverse skyline queries. In: 29th IEEE International Conference on Data Engineering, ICDE 2013, Brisbane, Australia, April 8-12, 2013, pp 973–984 (2013)Google Scholar
  16. 16.
    Jagadish, H.V., Chapman, A., Elkiss, A., Jayapandian, M., Li, Y., Nandi, A., Yu, C.: Making database systems usable. In: SIGMOD, pp 13–24 (2007)Google Scholar
  17. 17.
    Kalinin, A., Çetintemel, U., Zdonik, S.B.: Interactive data exploration using semantic windows. In: International Conference on Management of Data, SIGMOD 2014, Snowbird, UT, USA, June 22-27, 2014, pp 505–516 (2014)Google Scholar
  18. 18.
    Li, F., Jagadish, H.V.: Usability, databases, and HCI. IEEE Data Eng. Bull. 35(3), 37–45 (2012)Google Scholar
  19. 19.
    Li, J., Liu, C., Yu, J.X.: Context-based diversification for keyword queries over XML data. IEEE Trans. Knowl Data Eng. 27(3), 660–672 (2015)CrossRefGoogle Scholar
  20. 20.
    Li, J., Liu, C., Zhou, R., Wang, W.: XML Keyword search with promising result type recommendations. World Wide Web 17(1), 127–159 (2014)CrossRefGoogle Scholar
  21. 21.
    Mishra, C., Koudas, N.: Interactive query refinement. In: EDBT, pp 862–873 (2009)Google Scholar
  22. 22.
    Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: ACM Conference on Digital Libraries, pp 195–204 (2000)Google Scholar
  23. 23.
    Nambiar, U., Kambhampati, S.: Answering imprecise queries over autonomous web databases. In: ICDE (2006)Google Scholar
  24. 24.
    Naseriparsa, M., Islam, M.S., Liu, C., Moser, I.: No-but-semantic-match: computing semantically matched xml keyword search results. World Wide Web, 1–35 (2017)Google Scholar
  25. 25.
    Palmisano, C., Tuzhilin, A., Gorgoglione, M.: Using context to improve predictive modeling of customers in personalization applications. IEEE Trans. Knowl. Data Eng. 20(11), 1535–1549 (2008)CrossRefGoogle Scholar
  26. 26.
    Pazzani, M.J., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27(3), 313–331 (1997)CrossRefGoogle Scholar
  27. 27.
    Schenkel, R., Theobald, A., Weikum, G.: Semantic similarity search on semistructured data with the XXL search engine. Inf. Retr. 8(4), 521–545 (2005)CrossRefGoogle Scholar
  28. 28.
    Sun, C., Chan, C.-Y., Goenka, A.K.: Multiway Slca-based keyword search in xml data. In: World Wide Web, pp 1043–1052 (2007)Google Scholar
  29. 29.
    Sun, J., Xu, J., Zheng, K., Liu, C.: Interactive spatial keyword querying with semantics. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, Singapore, November 06 - 10, 2017, pp 1727–1736 (2017)Google Scholar
  30. 30.
    Xu, Y., Papakonstantinou, Y.: Efficient keyword search for smallest lcas in xml databases. In: SIGMOD, pp 527–538 (2005)Google Scholar
  31. 31.
    Xu, Y., Papakonstantinou, Y.: Efficient lca based keyword search in xml data. In: EDBT, pp 535–546 (2008)Google Scholar
  32. 32.
    Zhou, R., Liu, C., Li, J.: Fast elca computation for keyword queries on xml data. In: EDBT, pp 549–560 (2010)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Mehdi Naseriparsa
    • 1
    Email author
  • Chengfei Liu
    • 1
  • Md. Saiful Islam
    • 2
  • Rui Zhou
    • 1
  1. 1.Swinburne University of TechnologyMelbourneAustralia
  2. 2.Griffith UniversityGold CoastAustralia

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