Rules for Inducing Hierarchies from Social Tagging Data

Dong, Hang ORCID: 0000-0001-6828-6891, Wang, Wei and Coenen, Frans ORCID: 0000-0003-1026-6649 (2018) Rules for Inducing Hierarchies from Social Tagging Data. .

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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.

Item Type:	Conference or Workshop Item (Unspecified)
Depositing User:	Symplectic Admin
Date Deposited:	08 Jun 2018 10:38
Last Modified:	19 Jan 2023 01:32
DOI:	10.1007/978-3-319-78105-1_38
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3022332