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Rules for Inducing Hierarchies from Social Tagging Data

  • Hang DongEmail author
  • Wei Wang
  • Frans Coenen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10766)

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 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.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of LiverpoolLiverpoolUK
  2. 2.Department of Computer Science and Software EngineeringXi’an Jiaotong-Liverpool UniversitySuzhouChina

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