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Coverage-Oriented Diversification of Keyword Search Results on Graphs

  • Ming ZhongEmail author
  • Ying Wang
  • Yuanyuan Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10828)

Abstract

Query result diversification has drawn great research interests in recent years. Most previous work focuses on finding a locally diverse subset of a given finite result set, in which the results are as dissimilar to each other as possible. However, such a setup may not always hold. Firstly, we may need the result set to be globally diverse with respect to all possible demands behind a given query. Secondly, the result set may not be given before diversification. In this paper, we address these two problems in the scenario of keyword search on graphs. We first reasonably formalize a problem of coverage-oriented diversified keyword search on graphs. It aims to find both locally and globally diverse and also relevant results simultaneously while searching on graphs. The global diversity is defined as a query-dependent metric called coverage, which dynamically assigns weights to potential query demands with respect to their topological distances to the given keywords. Then, we present a search algorithm to solve our problem. It guarantees to return the optimal diverse result set, and can eliminate unnecessary and redundant diversity computation. Lastly, we perform both effectiveness and efficiency evaluation of our approach on DBPedia. Compared with the local diversification approach, our approach can improve the coverage and reduce the redundancy of search results remarkably.

Notes

Acknowledgement

This paper was supported by National Natural Science Foundation of China under Grant No. 61202036, 61502349 and 61572376 and Natural Science Foundation of Hubei Province under Grant No. 2018CFB616.

References

  1. 1.
    Agrawal, R., Gollapudi, S., Halverson, A., et al.: Diversifying search results. In: ACM International Conference on Web Search and Data Mining, pp. 5–14. ACM (2009)Google Scholar
  2. 2.
    Alekseev, V.E.: An upper bound for the number of maximal independent sets in a graph. Discrete Math. Appl. 17(4), 355–359 (2007)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Angel, A., Koudas, N.: Efficient diversity-aware search. In: ACM SIGMOD International Conference on Management of Data, pp. 781–792. ACM (2011)Google Scholar
  4. 4.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. 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/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-76298-0_52CrossRefGoogle Scholar
  5. 5.
    Hulgeri, A., Nakhe, C.: Keyword searching and browsing in databases using BANKS. In: Proceedings of International Conference on Data Engineering, pp. 431–440. IEEE (2002)Google Scholar
  6. 6.
    Capannini, G., Nardini, F.M., Perego, R., et al.: Efficient diversification of web search results. Proc. VLDB Endow. 4(7), 451–459 (2011)CrossRefGoogle Scholar
  7. 7.
    Demidova, E., Fankhauser, P., Zhou, X., et al.: DivQ: diversification for keyword search over structured databases, pp. 331–338. ACM (2010)Google Scholar
  8. 8.
    Deng, T., Fan, W.: On the complexity of query result diversification. ACM (2014)Google Scholar
  9. 9.
    Drosou, M., Pitoura, E.: DisC diversity: result diversification based on dissimilarity and coverage. Proc. VLDB Endow. 6(1), 13–24 (2012)CrossRefGoogle Scholar
  10. 10.
    Fraternali, P., Martinenghi, D., Tagliasacchi, M.: Top-k bounded diversification. In: ACM SIGMOD International Conference on Management of Data, pp. 421–432. ACM (2012)Google Scholar
  11. 11.
    Golenberg, K., Kimelfeld, B., Sagiv, Y.: Keyword proximity search in complex data graphs. In: ACM SIGMOD International Conference on Management of Data, pp. 927–940. ACM (2008)Google Scholar
  12. 12.
    Gollapudi, S., Sharma, A.: An axiomatic approach for result diversification, pp. 381–390 (2009)Google Scholar
  13. 13.
    He, H., Wang, H., Yang, J., et al.: BLINKS: ranked keyword searches on graphs. In: ACM SIGMOD International Conference on Management of Data, pp. 305–316. ACM (2007)Google Scholar
  14. 14.
    Hu, S., Dou, Z., Wang, X., et al.: Search result diversification based on hierarchical intents, pp. 63–72 (2015)Google Scholar
  15. 15.
    Kacholia, V., Pandit, S., Chakrabarti, S., et al.: Bidirectional expansion for keyword search on graph databases. In: International Conference on Very Large Data Bases, Trondheim, Norway, 30 August - September, pp. 505–516. DBLP (2005)Google Scholar
  16. 16.
    Li, G., Ooi, B.C., Feng, J., et al.: EASE: an effective 3-in-1 keyword search method for unstructured, semi-structured and structured data. In: ACM SIGMOD International Conference on Management of Data, pp. 903–914. ACM (2008)Google Scholar
  17. 17.
    Liu, Z., Sun, P., Chen, Y.: Structured search result differentiation. VLDB Endow. 313–324 (2009)Google Scholar
  18. 18.
    Qin, L., Yu, J.X., Chang, L.: Diversifying top-k results. Proc. VLDB Endow. 5(11), 1124–1135 (2012)CrossRefGoogle Scholar
  19. 19.
    Rafiei, D., Bharat, K., Shukla, A.: Diversifying web search results. In: International Conference on World Wide Web, WWW 2010, Raleigh, North Carolina, USA, April 2010, pp. 781–790. DBLP (2010)Google Scholar
  20. 20.
    Tran, T., Wang, H., Rudolph, S., et al.: Top-k exploration of query candidates for efficient keyword search on graph-shaped (RDF) data. In: IEEE International Conference on Data Engineering, pp. 405–416. IEEE (2009)Google Scholar
  21. 21.
    Vee, E., Srivastava, U., Shanmugasundaram, J., et al.: Efficient computation of diverse query results. In: IEEE International Conference on Data Engineering, pp. 228–236. IEEE (2008)Google Scholar
  22. 22.
    Vieira, M., Razente, H., et al.: On query result diversification. In: ICDE Proceedings, pp. 1163–1174 (2011)Google Scholar
  23. 23.
    Wu, Y., Yang, S., Srivatsa, M., et al.: Summarizing answer graphs induced by keyword queries. Proc. VLDB Endow. 6(14), 1774–1785 (2013)CrossRefGoogle Scholar
  24. 24.
    Qin, L., Yu, J.X., Chang, L.: Keyword search in databases: the power of RDBMS. In: ACM SIGMOD International Conference on Management of Data, SIGMOD, Providence, Rhode Island, USA, 29 June - July, pp. 681–694. DBLP (2009)Google Scholar
  25. 25.
    Cong, Y., Lakshmanan, L., et al.: It takes variety to make a world: diversification in recommender systems. In: EDBT Proceedings, pp. 368–378 (2009)Google Scholar
  26. 26.
    Zhao, F., Zhang, X., Tung, A.K.H., et al.: BROAD: diversified keyword search in databases. Proc. VLDB Endow. 4(12), 1355–1358 (2012)Google Scholar
  27. 27.
    Zheng, K., Wang, H., Qi, Z., et al.: A survey of query result diversification. Knowl. Inf. Syst. 51(1), 1–36 (2017)CrossRefGoogle Scholar
  28. 28.
    Zou, L., Huang, R., Wang, H., et al.: Natural language question answering over RDF: a graph data driven approach. ACM (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of ComputerWuhan UniversityWuhanChina

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