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Exploration on Similar Spatial Textual Objects Retrieval

  • Conference paper
Behavior and Social Computing (BSIC 2013, BSI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8178))

Abstract

Effective and efficient retrieval of similar spatial textual objects plays an important role for many location based applications, such as Foursquare, Yelp, and so forth. Although there are many studies exploring on this issue, most of them focus on how to integrate spatial and textual information to efficiently retrieve top-k results yet few of them address the effectiveness issue. In this paper, we propose a semantic aware strategy which can effectively and efficiently retrieve the top-k similar spatial textual objects based on a general framework. Extensive experimental evaluation demonstrates that the performance of our proposal outperforms the state-of-the-art approach.

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References

  1. Chen, Y.-Y., Suel, T., Markowetz, A.: Efficient query processing in geographic web search engines. In: SIGMOD, pp. 277–288.

    Google Scholar 

  2. Cheng, Z., Caverlee, J., Lee, K., Sui, D.Z.: Exploring millions of footprints in location sharing services. In: ICWSM, pp. 81–88 (2011)

    Google Scholar 

  3. Christoforaki, M., He, J., Dimopoulos, C., Markowetz, A., Suel, T.: Text vs. space: efficient geo-search query processing. In: CIKM, pp. 423–432 (2011)

    Google Scholar 

  4. 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 

  5. Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. In: PODS, pp. 102–113 (2001)

    Google Scholar 

  6. Felipe, I.D., Hristidis, V., Rishe, N.: Keyword search on spatial databases. In: ICDE, pp. 656–665 (2008)

    Google Scholar 

  7. Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: SIGMOD, pp. 47–57 (1984)

    Google Scholar 

  8. Hjaltason, G.R., Samet, H.: Distance browsing in spatial databases. TODS 24(2), 265–318 (1999)

    Article  Google Scholar 

  9. Islam, A., Inkpen, D.: Semantic text similarity using corpus-based word similarity and string similarity. TKDD 2(2), 1–25 (2008)

    Article  Google Scholar 

  10. Khodaei, A., Shahabi, C., Li, C.: Hybrid indexing and seamless ranking of spatial and textual features of web documents. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds.) DEXA 2010, Part I. LNCS, vol. 6261, pp. 450–466. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  11. Li, Y., McLean, D., Bandar, Z., O’Shea, J., Crockett, K.A.: Sentence similarity based on semantic nets and corpus statistics. TKDE 18(8), 1138–1150 (2006)

    Google Scholar 

  12. Li, Z., Lee, K.C.K., Zheng, B., Lee, W.C., Lee, D.L., Wang, X.: Ir-tree: An efficient index for geographic document search. TKDE 23(4), 585–599 (2011)

    Google Scholar 

  13. Martins, B., Silva, M.J., Andrade, L.: Indexing and ranking in geo-ir systems. In: GIS, pp. 31–34 (2005)

    Google Scholar 

  14. Mihalcea, R., Corley, C., Strapparava, C.: Corpus-based and knowledge-based measures of text semantic similarity. In: AAAI, pp. 775–780 (2006)

    Google Scholar 

  15. 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)

    Chapter  Google Scholar 

  16. Strohman, T., Turtle, H., Croft, W.B.: Optimization strategies for complex queries. In: SIGIR, pp. 219–225 (2005)

    Google Scholar 

  17. Tsatsaronis, G., Varlamis, I., Vazirgiannis, M.: Text relatedness based on a word thesaurus. JAIR 37(1), 1–40 (2010)

    MATH  Google Scholar 

  18. Zhang, D., Chee, Y.M., Mondal, A., Tung, A.K.H., Kitsuregawa, M.: Keyword search in spatial databases: Towards searching by document. In: ICDE, pp. 688–699 (2009)

    Google Scholar 

  19. Zhou, Y., Xie, X., Wang, C., Gong, Y., Ma, W.Y.: Hybrid index structures for location-based web search. In: CIKM, pp. 155–162 (2005)

    Google Scholar 

  20. Zobel, J., Moffat, A.: Inverted files for text search engines. ACM Comput. Surv. 38(2) (2006)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Industrial Science, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, Japan

    Yanhui Gu, Zhenglu Yang, Miyuki Nakano & Masaru Kitsuregawa

  2. National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan

    Masaru Kitsuregawa

Authors
  1. Yanhui Gu
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  2. Zhenglu Yang
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  3. Miyuki Nakano
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  4. Masaru Kitsuregawa
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Editor information

Editors and Affiliations

  1. Advanced Analytics Institute, University of Technology, 2-12 Blackfriars Street, Chippendale, Blackfriars Campus, NSW 2008, Sydney, Australia

    Longbing Cao

  2. Institute of Scientific and Industrial Research, Osaka University, Japan

    Hiroshi Motoda

  3. Department of Computer Science, University of Minnesota, USA

    Jaideep Srivastava

  4. School of Information Systems, Singapore Management University, 80 Stamford Road, 178902, Singapore

    Ee-Peng Lim

  5. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Irwin King

  6. Department of Computer Science, University of Illinois at Chicago, 851 S. Morgan St., Rm 1138 SEO, 60607, Chicago, IL, USA

    Philip S. Yu

  7. Leibniz Universität Hannover, Germany

    Wolfgang Nejdl

  8. Advanced Analytics Institute, University of Technology, Sydney, Australia

    Guandong Xu

  9. Deakin University, Burwood, VIC, Australia

    Gang Li

  10. Shanghai Jiao Tong University, 200240, Shanghai, China

    Ya Zhang

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© 2013 Springer International Publishing Switzerland

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Cite this paper

Gu, Y., Yang, Z., Nakano, M., Kitsuregawa, M. (2013). Exploration on Similar Spatial Textual Objects Retrieval. In: Cao, L., et al. Behavior and Social Computing. BSIC BSI 2013 2013. Lecture Notes in Computer Science(), vol 8178. Springer, Cham. https://doi.org/10.1007/978-3-319-04048-6_21

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  • DOI: https://doi.org/10.1007/978-3-319-04048-6_21

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04047-9

  • Online ISBN: 978-3-319-04048-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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