Rules for Inducing Hierarchies from Social Tagging Data
H. Dong, W. Wang, and F. Coenen. Transforming Digital Worlds, page 345--355. Cham, Springer International Publishing, (2018)
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.
Description
Rules for Inducing Hierarchies from Social Tagging Data | SpringerLink
%0 Conference Paper
%1 10.1007/978-3-319-78105-1_38
%A Dong, Hang
%A Wang, Wei
%A Coenen, Frans
%B Transforming Digital Worlds
%C Cham
%D 2018
%E Chowdhury, Gobinda
%E McLeod, Julie
%E Gillet, Val
%E Willett, Peter
%I Springer International Publishing
%K folksonomy fuzzy_set_inclusion hierarchical_relation myown ontology_learning probabilistic_association probabilistic_topic_model rules social_tagging_data social_tags subsumption_relations
%P 345--355
%T Rules for Inducing Hierarchies from Social Tagging Data
%X 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.
%@ 978-3-319-78105-1
@inproceedings{10.1007/978-3-319-78105-1_38,
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.},
added-at = {2018-03-20T21:11:48.000+0100},
address = {Cham},
author = {Dong, Hang and Wang, Wei and Coenen, Frans},
biburl = {https://www.bibsonomy.org/bibtex/2e41f059d3b72c2bbfdb0063eaf8772b0/hangdong},
booktitle = {Transforming Digital Worlds},
description = {Rules for Inducing Hierarchies from Social Tagging Data | SpringerLink},
editor = {Chowdhury, Gobinda and McLeod, Julie and Gillet, Val and Willett, Peter},
interhash = {dcba4a6f7e57e1f56250443372e4b600},
intrahash = {e41f059d3b72c2bbfdb0063eaf8772b0},
isbn = {978-3-319-78105-1},
keywords = {folksonomy fuzzy_set_inclusion hierarchical_relation myown ontology_learning probabilistic_association probabilistic_topic_model rules social_tagging_data social_tags subsumption_relations},
pages = {345--355},
publisher = {Springer International Publishing},
timestamp = {2018-03-20T21:11:48.000+0100},
title = {Rules for Inducing Hierarchies from Social Tagging Data},
year = 2018
}