TY - GEN
T1 - Facilitating knowledge exploration in folksonomies
T2 - 2nd IEEE International Conference on Social Computing, SocialCom 2010, 2nd IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2010
AU - Fu, Wai Tat
AU - Dong, Wei
PY - 2010
Y1 - 2010
N2 - We developed user models of knowledge exploration in a social tagging system to test the expertise rankings generated by a link-structure method and a semantic-structure method. The link-structure method assumed a referential definition of expertise, in which experts were users who tagged resources that were frequently tagged by other experts; the semantic-structure method assumed a representational definition of expertise, in which experts were users who had better knowledge of a particular domain and were better at assigning distinctive tags associated with certain domain-specific resources. Simulations results showed that the two methods of expert identification, although based on different assumptions, were in general consistent but did show significant differences. As expected, the link-structure method was better at facilitating exploration of popular "hot" topics than the semantic-structure method. However, the semantic-structure method was better at guiding users to find less popular "cold" topics than the link-structure method. Resources tagged by domain experts could contain cold topics that were associated with high quality tags, but these resources were less likely highlighted by the link-structure method. We argue that to facilitate knowledge exploration in social tagging systems, it is important to keep a good balance between helping user to follow hot topics and to discover cold topics by including expertise rankings generated by both link and semantic structures.
AB - We developed user models of knowledge exploration in a social tagging system to test the expertise rankings generated by a link-structure method and a semantic-structure method. The link-structure method assumed a referential definition of expertise, in which experts were users who tagged resources that were frequently tagged by other experts; the semantic-structure method assumed a representational definition of expertise, in which experts were users who had better knowledge of a particular domain and were better at assigning distinctive tags associated with certain domain-specific resources. Simulations results showed that the two methods of expert identification, although based on different assumptions, were in general consistent but did show significant differences. As expected, the link-structure method was better at facilitating exploration of popular "hot" topics than the semantic-structure method. However, the semantic-structure method was better at guiding users to find less popular "cold" topics than the link-structure method. Resources tagged by domain experts could contain cold topics that were associated with high quality tags, but these resources were less likely highlighted by the link-structure method. We argue that to facilitate knowledge exploration in social tagging systems, it is important to keep a good balance between helping user to follow hot topics and to discover cold topics by including expertise rankings generated by both link and semantic structures.
KW - Expert identification
KW - Exploratory search
KW - Knowledge exploration
KW - Social tagging
KW - User model
UR - http://www.scopus.com/inward/record.url?scp=78649261401&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78649261401&partnerID=8YFLogxK
U2 - 10.1109/SocialCom.2010.73
DO - 10.1109/SocialCom.2010.73
M3 - Conference contribution
AN - SCOPUS:78649261401
SN - 9780769542119
T3 - Proceedings - SocialCom 2010: 2nd IEEE International Conference on Social Computing, PASSAT 2010: 2nd IEEE International Conference on Privacy, Security, Risk and Trust
SP - 459
EP - 464
BT - Proceedings - SocialCom 2010
Y2 - 20 August 2010 through 22 August 2010
ER -