Modeling Consensus Semantics in Social Tagging Systems

In social tagging systems, people can annotate arbitrary tags to online data to categorize and index them. However, the lack of the "a priori" set of words makes it difficult for people to reach consensus about the semantics of tags and how to categorize data. Ontologies based approaches can help reaching such consensus, but they are still facing problems such as inability of model ambiguous and new concepts properly. For tags that are used very few times, since they can only be used in very specific contexts, their semantics are very clear and detailed. Although people have no consensus on these tags, it is still possible to leverage these detailed semantics to model the other tags. In this paper we introduce a random walk and spreading activation like model to represent the semantics of tags using semantics of unpopular tags. By comparing the proposed model to the classic Latent Semantic Analysis approach in a concept clustering task, we show that the proposed model can properly capture the semantics of tags.