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Map search via a factor graph model

Published: 27 October 2013 Publication History

Abstract

Map search has received considerable attention in recent years. With map search, users can specify target locations with textual queries. However, these queries do not always include well-formed addresses or place names. They may contain transpositions, misspellings, fragments and so on. Queries may significantly differ from items stored in the spatial database. In this paper, we propose to connect this task to the semi-structured retrieval problem. A novel factor graph-based semi-structured retrieval framework is introduced to incorporate concept weighting, attribute selection, and word-based similarity metrics together. We randomly sampled a number of queries from logs of a commercial map search engine and manually labeled their categories and relevant results for analysis and evaluation. The results of several experimental comparisons demonstrate that our method outperforms both state-of-the-art semi-structured retrieval methods and some commercial systems in retrieving freeform location queries.

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Cited By

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  • (2017)A holistic approach to aligning geospatial data with multidimensional similarity measuringInternational Journal of Digital Earth10.1080/17538947.2017.135968811:8(845-862)Online publication date: 2-Aug-2017
  • (2016)A holistic framework of geographical semantic web aligningProceedings of the 10th Workshop on Geographic Information Retrieval10.1145/3003464.3003465(1-10)Online publication date: 31-Oct-2016

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  1. Map search via a factor graph model

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    cover image ACM Conferences
    CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management
    October 2013
    2612 pages
    ISBN:9781450322638
    DOI:10.1145/2505515
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    Publication History

    Published: 27 October 2013

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    Author Tags

    1. factor graph model
    2. map search
    3. query representation

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    CIKM'13: 22nd ACM International Conference on Information and Knowledge Management
    October 27 - November 1, 2013
    California, San Francisco, USA

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    CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;
    Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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    View all
    • (2017)A holistic approach to aligning geospatial data with multidimensional similarity measuringInternational Journal of Digital Earth10.1080/17538947.2017.135968811:8(845-862)Online publication date: 2-Aug-2017
    • (2016)A holistic framework of geographical semantic web aligningProceedings of the 10th Workshop on Geographic Information Retrieval10.1145/3003464.3003465(1-10)Online publication date: 31-Oct-2016

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