TY - GEN
T1 - Learning structured knowledge from social tagging data
T2 - IEEE International Conference on Smart City, SmartCity 2015
AU - Dong, Hang
AU - Wang, Wei
AU - Liang, Hai Ning
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Folksonomy
KW - Knowledge Engineering
KW - Knowledge Extraction
KW - Knowledge Organization Systems
KW - Ontology Learning
KW - Social Media data
KW - Social tagging data
UR - http://www.scopus.com/inward/record.url?scp=84973864233&partnerID=8YFLogxK
U2 - 10.1109/SmartCity.2015.89
DO - 10.1109/SmartCity.2015.89
M3 - Conference Proceeding
AN - SCOPUS:84973864233
T3 - Proceedings - 2015 IEEE International Conference on Smart City, SmartCity 2015, Held Jointly with 8th IEEE International Conference on Social Computing and Networking, SocialCom 2015, 5th IEEE International Conference on Sustainable Computing and Communications, SustainCom 2015, 2015 International Conference on Big Data Intelligence and Computing, DataCom 2015, 5th International Symposium on Cloud and Service Computing, SC2 2015
SP - 307
EP - 314
BT - Proceedings - 2015 IEEE International Conference on Smart City, SmartCity 2015, Held Jointly with 8th IEEE International Conference on Social Computing and Networking, SocialCom 2015, 5th IEEE International Conference on Sustainable Computing and Communications, SustainCom 2015, 2015 International Conference on Big Data Intelligence and Computing, DataCom 2015, 5th International Symposium on Cloud and Service Computing, SC2 2015
A2 - Liu, Xingang
A2 - Wang, Peicheng
A2 - Wang, Yufeng
A2 - Dong, Mianxiong
A2 - Hsu, Robert C. H.
A2 - Xia, Feng
A2 - Deng, Yuhui
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 December 2015 through 21 December 2015
ER -