Multi-objective Spatial Keyword Query with Semantics

  • Jing Chen
  • Jiajie XuEmail author
  • Chengfei Liu
  • Zhixu Li
  • An Liu
  • Zhiming Ding
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10178)


Multi-objective spatial keyword query finds broad applications in map services nowadays. It aims to find a set of objects that can cover all query objectives and are reasonably distributed in spatial. However, existing approaches mainly take the coverage of query keywords into account, while leaving the semantics behind the textual data to be largely ignored. This limits us to return those rational results that are synonyms but morphologically different. To address this problem, this paper studies the problem of multi-objective spatial keyword query with semantics. It targets to return the object set that is optimum regarding to both spatial proximity and semantic relevance. We propose an indexing structure called LIR-tree, as well as two advanced query processing approaches to achieve efficient query processing. Empirical study based on real dataset demonstrates the good effectiveness and efficiency of our proposed algorithms.



This work was partially supported by Chinese NSFC project under grant numbers 61402312, 61232006, 61402313, 61572336, 61502324, 61572335, and Australia Research Council discovery projects under grant numbers DP140103499, DP160102412.


  1. 1.
    Barbieri, D.F., Braga, D., Ceri, S., Valle, E.D., Grossniklaus, M.: C-SPARQL: SPARQL for continuous querying. In: WWW, pp. 1061–1062 (2009)Google Scholar
  2. 2.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  3. 3.
    Buhler, J.: Efficient large-scale sequence comparison by locality-sensitive hashing. Bioinformatics 17(5), 419–428 (2001)CrossRefGoogle Scholar
  4. 4.
    Cao, X., Cong, G., Jensen, C.S.: Retrieving top-k prestige-based relevant spatial web objects. PVLDB 3(1), 373–384 (2010)Google Scholar
  5. 5.
    Cao, X., Cong, G., Jensen, C.S., Ooi, B.C.: Collective spatial keyword querying. In: SIGMOD, pp. 373–384 (2011)Google Scholar
  6. 6.
    Chen, Y.-Y., Suel, T., Markowetz, A.: Efficient query processing in geographic web search engines. In: SIGMOD Conference, pp. 277–288 (2006)Google Scholar
  7. 7.
    Cong, G., Jensen, C.S., Wu, D.: Efficient retrieval of the top-k most relevant spatial web objects. PVLDB 2(1), 337–348 (2009)Google Scholar
  8. 8.
    Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Symposium on Computational Geometry, pp. 253–262 (2004)Google Scholar
  9. 9.
    De Felipe, I., Hristidis, V., Rishe, N.: Keyword search on spatial databases. In: ICDE, pp. 656–665 (2008)Google Scholar
  10. 10.
    Jiang, H., Zhao, P., Sheng, V.S., Xu, J., Liu, A., Wu, J., Cui, Z.: An efficient location-aware top-k subscription matching for publish/subscribe with Boolean expressions. In: Navathe, S.B., Wu, W., Shekhar, S., Du, X., Wang, X.S., Xiong, H. (eds.) DASFAA 2016. LNCS, vol. 9643, pp. 335–350. Springer, Cham (2016). doi: 10.1007/978-3-319-32049-6_21 CrossRefGoogle Scholar
  11. 11.
    Huiqi, H., Liu, Y., Li, G., Feng, J., Tan, K.-L.: A location-aware publish, subscribe framework for parameterized spatio-textual subscriptions. In: ICDE, pp. 711–722 (2015)Google Scholar
  12. 12.
    Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: STOC, pp. 604–613 (1998)Google Scholar
  13. 13.
    Jin, J., Szekely, P.: Interactive querying of temporal data using a comic strip metaphor. In: IEEE VAST, pp. 163–170 (2010)Google Scholar
  14. 14.
    Li, F., Yao, B., Tang, M., Hadjieleftheriou, M.: Spatial approximate string search. IEEE Trans. Knowl. Data Eng. 25(6), 1394–1409 (2013)CrossRefGoogle Scholar
  15. 15.
    Li, G., Wang, Y., Wang, T., Feng, J.: Location-aware publish, subscribe. In: KDD, pp. 802–810 (2013)Google Scholar
  16. 16.
    Rocha-Junior, J.B., Gkorgkas, O., Jonassen, S., Nørvåg, K.: Efficient processing of top-k spatial keyword queries. In: Pfoser, D., Tao, Y., Mouratidis, K., Nascimento, M.A., Mokbel, M., Shekhar, S., Huang, Y. (eds.) SSTD 2011. LNCS, vol. 6849, pp. 205–222. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-22922-0_13 CrossRefGoogle Scholar
  17. 17.
    Slaney, M., Casey, M.: Locality-sensitive hashing for finding nearest neighbors. IEEE Sig. Process. Mag. 25(2), 128–131 (2008)CrossRefGoogle Scholar
  18. 18.
    Tran, Q.T., Chan, C.-Y.: How to conquer why-not questions. In: SIGMOD, pp. 15–26 (2010)Google Scholar
  19. 19.
    Yao, B., Li, F., Hadjieleftheriou, M., Hou, K.: Approximate string search in spatial databases. In: ICDE, pp. 545–556 (2010)Google Scholar
  20. 20.
    Zhang, C., Zhang, Y., Zhang, W., Lin, X.: Inverted linear quadtree: efficient top k spatial keyword search. In: ICDE, pp. 901–912 (2013)Google Scholar
  21. 21.
    Zheng, B., Yuan, N.J., Zheng, K., Xie, X., Sadiq, S., Zhou, X.: Approximate keyword search in semantic trajectory database. In: ICDE 2015, pp. 975–986 (2015)Google Scholar
  22. 22.
    Zheng, K., Huang, Z., Zhou, A., Zhou, X.: Discovering the most influential sites over uncertain data: a rank-based approach. IEEE TKDE 24(12), 2156–2169 (2012)Google Scholar
  23. 23.
    Zheng, K., Han, S., Zheng, B., Shang, S., Jiajie, X., Liu, J., Zhou, X.: Interactive top-k spatial keyword queries. In: ICDE 2015, pp. 423–434 (2015)Google Scholar
  24. 24.
    Ding, Z., Xu, J., Yang, Q.: SeaCloudDM: a database cluster framework for managing and querying massive heterogeneous sensor sampling data. J. Supercomput. 66(3), 1260–1284 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jing Chen
    • 1
  • Jiajie Xu
    • 1
    Email author
  • Chengfei Liu
    • 2
  • Zhixu Li
    • 1
  • An Liu
    • 1
  • Zhiming Ding
    • 3
  1. 1.Department of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Faculty of SETSwinbourne University of TechnologyMelbourneAustralia
  3. 3.Department of Computer Science and TechnologyBeijing University of TechnologyBeijingChina

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