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Offline Strategies for Online Set Expansion

  • Author / Creator
    Zhou, Kai
  • Set expansion aims at expanding a given query seed set into a larger and more complete set by adding elements that are likely to belong to the same grouping as the elements of the query set. This thesis studies the problem of efficient set expansion; in particular, given a collection of data sets, each corresponding to an object grouping, and a query set, we develop offline strategies to preprocess and organize the data sets such that online set expansion queries can be answered efficiently. We show how those strategies can be tuned for different set expansion semantics. We also evaluate our algorithms on a real dataset, constructed from the Wikipedia tables.

  • Subjects / Keywords
  • Graduation date
    2016-06
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R36Q1SR16
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Rafiei, Davood (Computing Science)
  • Examining committee members and their departments
    • Sander, Joerg (Computing Science)
    • Rafiei, Davood (Computing Science)
    • Lu, Paul (Computing Science)