Offline Strategies for Online Set Expansion
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http://id.loc.gov/authorities/names/n79058482
Degree Level
Master's
Degree
Master of Science
Department
Department of Computing Science
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Abstract
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.
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http://purl.org/coar/resource_type/c_46ec
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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.
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Language
en