@inproceedings{51380491652a4f4c8404ab1b554335c9,
title = "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 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.",
author = "Hang Dong and Wei Wang and Frans Coenen",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-319-78105-1_38",
language = "English",
isbn = "9783319781044",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "345--355",
editor = "Gobinda Chowdhury and Julie McLeod and Val Gillet and Peter Willett",
booktitle = "Transforming Digital Worlds - 13th International Conference, iConference 2018, Proceedings",
address = "United Kingdom",
note = "13th International Conference on Transforming Digital Worlds, iConference 2018 ; Conference date: 25-03-2018 Through 28-03-2018",
}