• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    • Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • A Distance-Based Spelling Suggestion Method for XML Keyword Search

    Author(s)
    Li, S
    Wang, J
    Wang, K
    Li, J
    Griffith University Author(s)
    Wang, Kewen
    Wang, John
    Li, Jiang
    Li, Sheng
    Year published
    2012
    Metadata
    Show full item record
    Abstract
    We study the spelling suggestion problem for keyword search on XML documents. To address the problems in existing work, we propose a distance-based approach to suggesting meaningful query candidates for an issued query. Our approach uses distance to measure the relationship between keyword matching nodes, and ranks a candidate higher if there are closely-related nodes in the database that match the candidate. We design an efficient algorithm to generate top-k query candidates. Experiments with real datasets verified the effectiveness and efficiency of our approach.We study the spelling suggestion problem for keyword search on XML documents. To address the problems in existing work, we propose a distance-based approach to suggesting meaningful query candidates for an issued query. Our approach uses distance to measure the relationship between keyword matching nodes, and ranks a candidate higher if there are closely-related nodes in the database that match the candidate. We design an efficient algorithm to generate top-k query candidates. Experiments with real datasets verified the effectiveness and efficiency of our approach.
    View less >
    Conference Title
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume
    7532 LNCS
    Publisher URI
    http://islab.di.unimi.it/er2012/
    DOI
    https://doi.org/10.1007/978-3-642-34002-4_14
    Subject
    Information Retrieval and Web Search
    Publication URI
    http://hdl.handle.net/10072/48922
    Collection
    • Conference outputs

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander