Supporting Book Search: A Comprehensive Comparison of Tags vs. Controlled Vocabulary Metadata
- ,
- Vivien Petras
- Aalborg University,
- Humboldt University of Berlin
Research Output:
Journal Article or Conference Article in Journal
Journal article
Peer-reviewOpen access
Publication Information
Output type
Research Output:
Journal Article or Conference Article in Journal
Journal article
Peer-reviewOriginal language
EnglishPages from-to (Number of pages)
Pages 17-34 (18 pages)Journal (Volume, Issue Number)
Journal of Data and Information Management (Volume 1, Issue 1)Publication milestones
- Published - 29/09/2017
Publication status
Published - 29/09/2017
ISSN
2543-9251Publication IDs
- Scopus: 85057275610
Abstract
Book search is far from a solved problem. Complex information needs often go beyond bibliographic facts and cover a combination of different aspects, such as specific genres or plot elements, engagement or novelty. Conventional book metadata may not be sufficient to address these kinds of information needs. In this paper, we present a large-scale empirical comparison of the effectiveness of book metadata elements for searching complex information needs. Using a test collection of over 2 million book records and over 330 real-world book search requests, we perform a highly controlled and in-depth analysis of topical metadata, comparing controlled vocabularies with social tags. Tags perform better overall in this setting, but controlled vocabulary terms provide complementary information, which will improve a search. We analyze potential underlying factors that contribute to search performance, such as the relevance aspect(s) mentioned in a request or the type of book. In addition, we investigate the possible causes of search failure. We conclude that neither tags nor controlled vocabularies are wholly suited to handling the complex information needs in book search, which means that different approaches to describe topical information in books are needed.
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Final published version, 2.58 MB
License:CC BY-NC-ND, opens in new tab
