Rules for inducing hierarchies from social tagging data

Hang Dong*, Wei Wang, Frans Coenen

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

4 Citations (Scopus)


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 learned hierarchies were compared to some of the widely used reference concept hierarchies. We found that probabilistic association and set inclusion based rules helped produce better quality hierarchies according to the evaluation metrics.

Original languageEnglish
Title of host publicationTransforming Digital Worlds - 13th International Conference, iConference 2018, Proceedings
EditorsGobinda Chowdhury, Julie McLeod, Val Gillet, Peter Willett
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783319781044
Publication statusPublished - 2018
Event13th International Conference on Transforming Digital Worlds, iConference 2018 - Sheffield, United Kingdom
Duration: 25 Mar 201828 Mar 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10766 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Transforming Digital Worlds, iConference 2018
Country/TerritoryUnited Kingdom

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