On Efficient Spatial Keyword Querying with Semantics

  • Zhihu Qian
  • Jiajie Xu
  • Kai Zheng
  • Wei Sun
  • Zhixu Li
  • Haoming Guo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9643)


The fast development of GPS equipped devices has aroused widespread use of spatial keyword querying in location based services nowadays. Existing spatial keyword indexing and querying methodologies mainly focus on the spatial and textual similarities, while leaving the semantic understanding of keywords in spatial web objects and queries to be ignored. To address this issue, this paper studies the problem of semantic based spatial keyword querying. It seeks to return the k objects most similar to the query, subject to not only their spatial and textual properties, but also the coherence of their semantic meanings. To achieve that, we propose a novel indexing structure called NIQ-tree, which integrates spatial, textual and semantic information in a hierarchical manner, so as to prune the search space effectively in query processing. Extensive experiments are carried out to evaluate and compare it with other two baseline algorithms.


Spatial keyword query Query optimization Probabilistic topic model Semantic similarity 



This work was partially supported by Chinese NSFC project under grant numbers 61402312, 61402313, 61572335, 61232006, the Key Research Program of the Chinese Academy of Sciences under grant number KGZD-EW-102-3-3, and Collaborative Innovation Center of Novel Software Technology and Industrialization.


  1. 1.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  2. 2.
    Cao, X., Cong, G., Jensen, C.S.: Collective spatial keyword querying. In: SIGMOD (2011)Google Scholar
  3. 3.
    Chen, L., Cong, G., Jensen, C.S.: Spatial keyword query processing: An experimental evaluation. PVLDB 6(3), 217–228 (2013)Google Scholar
  4. 4.
    Chen, Q., Hu, H., Xu, J.: Authenticating top-k queries in location-based services with confidentiality. PVLDB 7(1), 49–60 (2013)Google Scholar
  5. 5.
    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
  6. 6.
    De Felipe, I., Hristidis, V., Rishe, N.: Keyword search on spatial databases. In: ICDE (2008)Google Scholar
  7. 7.
    Finkel, R.A., Bentley, J.L.: Quad trees a data structure for retrieval on composite keys. Acta informatica 4(1), 1–9 (1974)CrossRefzbMATHGoogle Scholar
  8. 8.
    Gravano, L., Ipeirotis, P.G.: Approximate string joins in a database (almost) for free. In: ICDE (2001)Google Scholar
  9. 9.
    Guo, L., Shao, J., Aung, H.H., Tan, K.-L.: Efficient continuous top-k spatial keyword queries on road networks. Geoinformatica 19(1), 29–60 (2015)CrossRefGoogle Scholar
  10. 10.
    Guttman, A.: R-trees: A dynamic index structure for spatial searching. In: SIGMOD (1984)Google Scholar
  11. 11.
    Har-Peled, S., Indyk, P., Motwani, R.: Approximate nearest neighbors: Towards removing the curse of dimensionality. In: ACM symposium on Theory of computing (1998)Google Scholar
  12. 12.
    Hua, W., Wang, Z., Wang, H., Zheng, K., Zhou, X.: Short text understanding through lexical-semantic analysis. In: ICDE (2015)Google Scholar
  13. 13.
    Jaccard, P.: Etude comparative de la distribution florale dans une portion des alpes et du jura. Impr. Corbaz (1901)Google Scholar
  14. 14.
    Jagadish, H.V., Ooi, B.C., Tan, K.-L.: idistance: An adaptive b+-tree based indexing method for nearest neighbor search. ACM TODS 30(2), 364–397 (2005)CrossRefGoogle Scholar
  15. 15.
    Levenshtein, V.I.: Binary codes with correction for deletions and insertions of the symbol 1. Problemy Peredachi Informatsii 1(1), 12–25 (1965)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Li, F., Yao, B., Tang, M.: Spatial approximate string search. TKDE 25(6), 1394–1409 (2013)Google Scholar
  17. 17.
    Li, G., Feng, J., Xu, J.: Desks: Direction-aware spatial keyword query. In: ICDE (2012)Google Scholar
  18. 18.
    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)CrossRefGoogle Scholar
  19. 19.
    Ukkonen, E.: Approximate string-matching with q-grams and maximal matches. Theor. Comput. Sci. 92, 191–211 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Wang, H., Zheng, K.: Sharkdb: An in-memory column-oriented trajectory storage. In: CIKM (2014)Google Scholar
  21. 21.
    Yao, B., Li, F., Hadjieleftheriou, M., Hou, K.: Approximate string search in spatial databases. In: ICDE (2010)Google Scholar
  22. 22.
    Zhang, C., Zhang, Y., Zhang, W., Lin, X.: Inverted linear quadtree: Efficient top k spatial keyword search. In: ICDE (2013)Google Scholar
  23. 23.
    Zheng, K., Huang, Z., Zhou, A.: Discovering the most influential sites over uncertain data: A rank based approach. TKDE 24(12), 2156–2169 (2012)Google Scholar
  24. 24.
    Zheng, K., Su, H.: Interactive top-k spatial keyword queries. In: ICDE (2015)Google Scholar
  25. 25.
    Zheng, K., Zheng, Y.: Online discovery of gathering patterns over trajectories. TKDE 26(8), 1974–1988 (2014)Google Scholar
  26. 26.
    Zheng, K., Zhou, X.: Spatial query processing for fuzzy objects. VLDB 21(5), 729–751 (2012)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zhihu Qian
    • 1
  • Jiajie Xu
    • 1
    • 2
  • Kai Zheng
    • 1
    • 2
  • Wei Sun
    • 1
  • Zhixu Li
    • 1
    • 2
  • Haoming Guo
    • 3
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.School of ITEEThe University of QueenslandBrisbaneAustralia
  3. 3.Institute of SoftwareChinese Academy of SciencesBeijingChina

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