Learning relations 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

2 Citations (Scopus)


An interesting research direction is to discover structured knowledge from user generated data. Our work aims to find relations among social tags and organise them into hierarchies so as to better support discovery and search for online users. We cast relation discovery in this context to a binary classification problem in supervised learning. This approach takes as input features of two tags extracted using probabilistic topic modelling, and predicts whether a broader-narrower relation holds between them. Experiments were conducted using two large, real-world datasets, the Bibsonomy dataset which is used to extract tags and their features, and the DBpedia dataset which is used as the ground truth. Three sets of features were designed and extracted based on topic distributions, similarity and probabilistic associations. Evaluation results with respect to the ground truth demonstrate that our method outperforms existing ones based on various features and heuristics. Future studies are suggested to study the Knowledge Base Enrichment from folksonomies and deep neural network approaches to process tagging data.

Original languageEnglish
Title of host publicationPRICAI 2018
Subtitle of host publicationTrends in Artificial Intelligence - 15th Pacific Rim International Conference on Artificial Intelligence, Proceedings
EditorsByeong-Ho Kang, Xin Geng
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319973036
Publication statusPublished - 2018
Event15th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018 - Nanjing, China
Duration: 28 Aug 201831 Aug 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11012 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2018


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