On Kernel Information Propagation for Tag Clustering in Social Annotation Systems

Publisher:
Springer Berlin / Heidelberg
Publication Type:
Conference Proceeding
Citation:
Knowlege-Based and Intelligent Information and Engineering Systems Lecture Notes in Computer Science, 2011, 6882 pp. 505 - 514
Issue Date:
2011-01
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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 re- source. Poor retrieval performance remains a major challenge of most social annotation systems resulting from the severe problems of ambigu- ity, redundancy and less semantic nature of tags. Clustering method is a useful approach to handle these problems in the social annotation sys- tems. 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.
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