Diversified Keyword Expansion on Multi-labeled Graphs

  • Mohammad Hossein Namaki
  • Yinghui Wu
  • Xin Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10987)


Keyword search has been widely adopted to explore graph data. Due to the intrinsic ambiguity of terms, it is desirable to develop query expansion techniques to find useful and diversified information progressively in large graphs. To support exploration with keywords, we study the problem of diversified keyword expansion in graphs. Given a set of validated content nodes in a graph, it is to find a set of terms that maximizes the aggregated relevance of the validated nodes. Moreover, the terms should be diversified to cover different search interests. We develop a fast stream-based (\(\frac{1}{2}\)-\(\epsilon \))-approximation to suggest diversified terms, which guarantees a linear scan of the terms in the content nodes up to a bounded area with small update cost. Using real-world graphs, we experimentally verify the effectiveness and efficiency of our algorithms, and their applications in knowledge base exploration.


Keyword query expansion Submodular maximization 



Namaki and Wu are supported in part by NSF IIS-1633629 and Huawei Innovation Research Program (HIRP).


  1. 1.
    Achiezra, H., Golenberg, K., Kimelfeld, B., Sagiv, Y.: Exploratory keyword search on data graphs. In: SIGMOD, pp. 1163–1166 (2010)Google Scholar
  2. 2.
    Akiba, T., Iwata, Y., Yoshida, Y.: Fast exact shortest-path distance queries on large networks. In: SIGMOD, pp. 349–360 (2013)Google Scholar
  3. 3.
    Badanidiyuru, A., Mirzasoleiman, B., Karbasi, A., Krause, A.: Streaming submodular maximization: massive data summarization on the fly. In: SIGKDD, pp. 671–680 (2014)Google Scholar
  4. 4.
    Bao, Z., Zeng, Y., Jagadish, H., Ling, T.W.: Exploratory keyword search with interactive input. In: SIGMOD, pp. 871–876 (2015)Google Scholar
  5. 5.
    Bhalotia, G., Hulgeri, A., Nakhe, C., Chakrabarti, S., Sudarshan, S.: Keyword searching and browsing in databases using BANKS. In: ICDE, pp. 431–440 (2002)Google Scholar
  6. 6.
    Bouchoucha, A., He, J., Nie, J.-Y.: Diversified query expansion using conceptnet. In: CIKM, pp. 1861–1864 (2013)Google Scholar
  7. 7.
    Carpineto, C., Romano, G.: A survey of automatic query expansion in information retrieval. CSUR 44, 1 (2012)CrossRefGoogle Scholar
  8. 8.
    De Nies, T., Beecks, C., Godin, F., De Neve, W., Stepien, G., Arndt, D., De Vocht, L., Verborgh, R., Seidl, T., Mannens, E., et al.: A distance-based approach for semantic dissimilarity in knowledge graphs. In: ICSC, pp. 254–257 (2016)Google Scholar
  9. 9.
    Ding, B., Yu, J.X., Wang, S., Qin, L., Zhang, X., Lin, X.: Finding top-k min-cost connected trees in databases. In: ICDE, pp. 836–845 (2007)Google Scholar
  10. 10.
    Gollapudi, S., Sharma, A.: An axiomatic approach for result diversification. In: WWW, pp. 381–390 (2009)Google Scholar
  11. 11.
    He, H., Wang, H., Yang, J., Yu, P.S.: BLINKS: ranked keyword searches on graphs. In: SIGMOD, pp. 305–316 (2007)Google Scholar
  12. 12.
    Jayaram, N., Khan, A., Li, C., Yan, X., Elmasri, R.: Querying knowledge graphs by example entity tuples. TKDE 27, 2797–2811 (2015)Google Scholar
  13. 13.
    Kacholia, V., Pandit, S., Chakrabarti, S., Sudarshan, S., Desai, R., Karambelkar, H.: Bidirectional expansion for keyword search on graph databases. In: VLDB (2005)Google Scholar
  14. 14.
    Kargar, M., An, A.: Keyword search in graphs: finding r-cliques. VLDB 4, 681–692 (2011)Google Scholar
  15. 15.
    Koutrika, G., Zadeh, Z.M., Garcia-Molina, H.: Data clouds: summarizing keyword search results over structured data. In: EDBT, pp. 391–402 (2009)Google Scholar
  16. 16.
    Ma, H., Lyu, M.R., King, I.: Diversifying query suggestion results. In: AAAI (2010)Google Scholar
  17. 17.
    Mishra, C., Koudas, N.: Interactive query refinement. In: EDBT (2009)Google Scholar
  18. 18.
    Mottin, D., Müller, E.: Graph exploration: from users to large graphs. In: PODS, pp. 1737–1740 (2017)Google Scholar
  19. 19.
    Namaki, M.H., Wu, Y., Zhang, X.: GExp: cost-aware graph exploration with keywords. In: SIGMOD (2018)Google Scholar
  20. 20.
    Tao, Y., Yu, J.X.: Finding frequent co-occurring terms in relational keyword search. In: EDBT, pp. 839–850 (2009)Google Scholar
  21. 21.
    Tong, H., Faloutsos, C., Pan, J.-Y.: Fast random walk with restart and its applications (2006)Google Scholar
  22. 22.
    Tran, Q.T., Chan, C.-Y.: How to ConQueR why-not questions. In: SIGMOD, pp. 15–26 (2010)Google Scholar
  23. 23.
    Tran, Q.T., Chan, C.-Y., Parthasarathy, S.: Query reverse engineering. VLDB 23, 721–746 (2014)CrossRefGoogle Scholar
  24. 24.
    Tran, T., Wang, H., Rudolph, S., Cimiano, P.: Top-k exploration of query candidates for efficient keyword search on graph-shaped (RDF) data. In: ICDE, pp. 405–416 (2009)Google Scholar
  25. 25.
    Wang, H., Aggarwal, C.C.: A survey of algorithms for keyword search on graph data. In: Aggarwal, C., Wang, H. (eds.) Managing and Mining Graph Data. ADBS, vol. 40, pp. 249–273. Springer, Boston (2010). Scholar
  26. 26.
    Yahya, M., Berberich, K., Ramanath, M., Weikum, G.: Exploratory querying of extended knowledge graphs. VLDB 9, 1521–1524 (2016)Google Scholar
  27. 27.
    Yang, S., Wu, Y., Sun, H., Yan, X.: Schemaless and structureless graph querying. PVLDB 7(7), 565–576 (2014)Google Scholar
  28. 28.
    Yao, J., Cui, B., Hua, L., Huang, Y.: Keyword query reformulation on structured data. In: ICDE, pp. 953–964 (2012)Google Scholar
  29. 29.
    Yu, J.X., Qin, L., Chang, L.: Keyword search in relational databases: a survey. IEEE Data Eng. Bull. 33, 67–78 (2010)Google Scholar
  30. 30.
    Zeng, Y., Bao, Z., Ling, T.W., Jagadish, H., Li, G.: Breaking out of the mismatch trap. In: ICDE, pp. 940–951 (2014)Google Scholar
  31. 31.
    Zheng, B., Zhang, W., Feng, X.F.B.: A survey of faceted search. J. Web Eng. 12, 041–064 (2013)Google Scholar
  32. 32.
    Zhou, Y., Cheng, H., Yu, J.X.: Graph clustering based on structural/attribute similarities. VLDB 2, 718–729 (2009)Google Scholar
  33. 33.
    Zhu, G., Iglesias, C.A.: Computing semantic similarity of concepts in knowledge graphs. TKDE 29, 72–85 (2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mohammad Hossein Namaki
    • 1
  • Yinghui Wu
    • 1
  • Xin Zhang
    • 1
  1. 1.Washington State UniversityPullmanUSA

Personalised recommendations