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Rules for inducing hierarchies from social tagging data

Abstract:

Automatic generation of hierarchies from social tags is a challenging task. We identified three rules, set inclusion, graph centrality and information-theoretic condition from the literature and proposed two new rules, fuzzy set inclusion and probabilistic association to induce hierarchical relations. We proposed an hierarchy generation algorithm, which can incorporate each rule with different data representations, i.e., resource and Probabilistic Topic Model based representations. The learne...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-319-78105-1_38

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
ORCID:
0000-0001-6828-6891
Publisher:
Springer Publisher's website
Host title:
iConference 2018: Transforming Digital Worlds
Series:
Lecture Notes in Computer Science
Volume:
10766
Pages:
345-355
Publication date:
2018-03-15
Acceptance date:
2018-01-01
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
ISBN:
978-3-319-78104-4
Language:
English
Keywords:
Pubs id:
1264320
Local pid:
pubs:1264320
Deposit date:
2022-09-13

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