Xu, Guandong, Zong, Yu, Pan, Rong, Dolog, Peter and Jin, Ping (2011) On kernel information propagation for tag clustering in social annotations systems. In: Knowledge based and intelligent information and engineering systems : 15th international conference, KES 2011, Kaiserslautern, Germany, September 12-14, 2011, proceedings, part II. König, Andreas, Dengel, Andreas, Hingelmann, Knut, Kise, Koichi, Howlett, Robert J, Jain, Lakhmi C, Goebel, Randy, Tanaka, Yuzuru, Wahlster, Wolfgang and Siekmann, Joerg, eds. Lecture notes in computer science (6882). Springer, Berlin, pp. 505-514.

Abstract

In social annotation systems, users label digital resources by using tags which are freely chosen textual descriptors. Tags are used to index, annotate and retrieve resource as an additional metadata of resource. Poor retrieval performance remains a major challenge of most social annotation systems resulting from the severe problems of ambiguity, redundancy and less semantic nature of tags. Clustering method is a useful approach to handle these problems in the social annotation systems. In this paper, we propose a novel clustering algorithm named kernel information propagation for tag clustering. This approach makes use of the kernel density estimation of the KNN neighbor directed graph as a start to reveal the prestige rank of tags in tagging data. The random walk with restart algorithm is then employed to determine the center points of tag clusters. The main strength of the proposed approach is the capability of partitioning tags from the perspective of tag prestige rank rather than the intuitive similarity calculation itself. Experimental studies on three real world datasets demonstrate the effectiveness and superiority of the proposed method.

Search Google Scholar

Repository staff login