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SKIF-P: a point-based indexing and ranking of web documents for spatial-keyword search

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Abstract

There is a significant commercial and research interest in location-based web search engines. Given a number of search keywords and one or more locations (geographical points) that a user is interested in, a location-based web search retrieves and ranks the most textually and spatially relevant web pages. In this type of search, both the spatial and textual information should be indexed. Currently, no efficient index structure exists that can handle both the spatial and textual aspects of data simultaneously and accurately. Existing approaches either index space and text separately or use inefficient hybrid index structures with poor performance and inaccurate results. Moreover, most of these approaches cannot accurately rank web-pages based on a combination of space and text and are not easy to integrate into existing search engines. In this paper, we propose a new index structure called Spatial-Keyword Inverted File for Points to handle point-based indexing of web documents in an integrated/efficient manner. To seamlessly find and rank relevant documents, we develop a new distance measure called spatial tf-idf. We propose four variants of spatial-keyword relevance scores and two algorithms to perform top-k searches. As verified by experiments, our proposed techniques outperform existing index structures in terms of search performance and accuracy.

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Notes

  1. We use terms documents and objects interchangeably throughout this paper.

  2. http://www.twitter.com

  3. http://www.flickr.com

  4. http://www.youtube.com

  5. http://www.nytimes.com

  6. http://www.yelp.com.

  7. http://www.foursquare.com

  8. We have to note that although representing locations as points are the current representation of choice on the web, it is not necessarily the most accurate one. For objects with spatial extents, the most accurate presentation is probably a polygon representation. We focus on points in this paper since 1) currently, spatial feature of most objects/documents on the web are represented as points, and 2) we have already showed how to handle cases when the spatial feature is a region in our previous work [1].

  9. For simplicity, we assume that each document has only one location. Multiple locations can be easily handled by using the same methods multiple times—once for each focal point.

  10. http://developer.yahoo.com/geo/placemaker/

  11. http://www.flickr.com/services/api/

  12. β = 0.1 and the number of clusters = 5.

  13. Since the results for DATASET1 and DATASET2 were very similar, we only report the results of the larger dataset—DATASET2.

  14. https://www.mturk.com/

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Acknowledgements

Ali Khodaei and Cyrus Shahabi’s research has been funded in part by NSF grants CNS-0831505 (CyberTrust) and IS-1115153, the USC Integrated Media Systems Center (IMSC), and unrestricted cash and equipment gifts from Google, Microsoft and Qualcomm. Chen Li is partially supported by the US NSF IIS 1030002 award and the National Natural Science Foundation of China (No. 61129002). Any opinions,findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Authors and Affiliations

  1. Department of Computer Science, University of Southern California, Los Angeles, CA, 90089, USA

    Ali Khodaei & Cyrus Shahabi

  2. Department of Computer Science, University of California-Irvine, Irvine, CA, 92697, USA

    Chen Li

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  1. Ali Khodaei
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  2. Cyrus Shahabi
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  3. Chen Li
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Correspondence to Ali Khodaei.

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Khodaei, A., Shahabi, C. & Li, C. SKIF-P: a point-based indexing and ranking of web documents for spatial-keyword search. Geoinformatica 16, 563–596 (2012). https://doi.org/10.1007/s10707-011-0142-7

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  • DOI: https://doi.org/10.1007/s10707-011-0142-7

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