%0 Conference Paper %1 7463743 %A Dong, H. %A Wang, W. %A Liang, H. N. %B 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity) %D 2015 %K conf folksonomy myown ontology_learning review social_tagging %P 307-314 %R 10.1109/SmartCity.2015.89 %T Learning Structured Knowledge from Social Tagging Data: A Critical Review of Methods and Techniques %U http://ieeexplore.ieee.org/xpl/abstractMetrics.jsp?tp=&arnumber=7463743&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7463743 %X For more than a decade, researchers have been proposing various methods and techniques to mine social tagging data and to learn structured knowledge. It is essential to conduct a comprehensive survey on the related work, which would benefit the research community by providing better understanding of the state-of-the-art and insights into the future research directions. The paper first defines the spectrum of Knowledge Organization Systems, from unstructured with less semantics to highly structured with richer semantics. It then reviews the related work by classifying the methods and techniques into two main categories, namely, learning term lists and learning relations. The method and techniques originated from natural language processing, data mining, machine learning, social network analysis, and the Semantic Web are discussed in detail under the two categories. We summarize the prominent issues with the current research and highlight future directions on learning constantly evolving knowledge from social media data.

@inproceedings{7463743, abstract = {For more than a decade, researchers have been proposing various methods and techniques to mine social tagging data and to learn structured knowledge. It is essential to conduct a comprehensive survey on the related work, which would benefit the research community by providing better understanding of the state-of-the-art and insights into the future research directions. The paper first defines the spectrum of Knowledge Organization Systems, from unstructured with less semantics to highly structured with richer semantics. It then reviews the related work by classifying the methods and techniques into two main categories, namely, learning term lists and learning relations. The method and techniques originated from natural language processing, data mining, machine learning, social network analysis, and the Semantic Web are discussed in detail under the two categories. We summarize the prominent issues with the current research and highlight future directions on learning constantly evolving knowledge from social media data.}, added-at = {2016-08-10T10:52:18.000+0200}, author = {Dong, H. and Wang, W. and Liang, H. N.}, biburl = {https://www.bibsonomy.org/bibtex/20e8438132d5cd2c1bf905762a5e9e41b/hangdong}, booktitle = {2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)}, description = {IEEE Xplore Abstract (Metrics) - Learning Structured Knowledge from Social Tagging Data: A Critical Review of Methods and Techniques}, doi = {10.1109/SmartCity.2015.89}, interhash = {05946f0a29f13c59e8f48a94c3a44b68}, intrahash = {0e8438132d5cd2c1bf905762a5e9e41b}, keywords = {conf folksonomy myown ontology_learning review social_tagging}, month = dec, pages = {307-314}, timestamp = {2016-08-10T10:52:18.000+0200}, title = {Learning Structured Knowledge from Social Tagging Data: A Critical Review of Methods and Techniques}, url = {http://ieeexplore.ieee.org/xpl/abstractMetrics.jsp?tp=&arnumber=7463743&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7463743}, year = 2015 }