document: Exploring social tagging graph for web object classification This paper studies web object classification problem with the novel exploration of social tags . Automatically classifying web objects into manageable semantic categories has long been a fundamental preprocess for indexing , browsing , searching , and mining these objects . The explosive growth of heterogeneous web objects , especially non-textual objects such as products , pictures , and videos , has made the problem of web classification increasingly challenging . Such objects often suffer from a lack of easy-extractable features with semantic information , interconnections between each other , as well as training examples with category labels . In this paper , we explore the social tagging data to bridge this gap . We cast web object classification problem as an optimization problem on a graph of objects and tags . We then propose an efficient algorithm which not only utilizes social tags as enriched semantic features for the objects , but also infers the categories of unlabeled objects from both homogeneous and heterogeneous labeled objects , through the implicit connection of social tags . Experiment results show that the exploration of social tags effectively boosts web object classification . Our algorithm significantly outperforms the state-of-the-art of general classification methods .
doc_bio_tags: O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O