Collaborative tagging systems such as Delicious provide a new means of organizing and sharing resources. They also allow users to search for documents relevant to a particular topic or for other users who are experts in a particular domain. Nevertheless, identifying relevant documents and knowledgeable users is not a trivial task, especially when the volume of documents is huge and there exist spamming activities. In this paper, we discuss the notions of experts and expertise in the context of collaborative tagging systems. We propose that the level of expertise of a user in a particular topic is mainly determined by two factors: (1) there should be a relationship of mutual reinforcement between the expertise of a user and the quality of a document; and (2) an expert should be one who tends to identify useful documents before other users discover them. We propose a graph-based algorithm, SPEAR (SPamming-resistant Expertise Analysis and Ranking), which implements the above ideas for ranking users in a collaborative tagging system. We carry out experiments on both simulated data sets and real-world data sets obtained from Delicious, and show that SPEAR is more resistant to spammers than other methods such as the HITS algorithm and simple statistical measures.

Existing Web search engines such as Google mostly adopt a keyword-based approach, which matches the keywords in a query sub- mitted by a user with the keywords characterising the indexed Web doc- uments, and is quite successful in general in helping users locate useful documents. However, when the keyword submitted by the user is am- biguous, the search result usually consists of documents related to var- ious meanings of the keyword, in which probably only one of them is interesting to the user. In this paper we attempt to provide a solution to this problem by using the semantics extracted from collaborative tag- ging in the social bookmarking site del.icio.us. For an ambiguous word, we extract sets of tags which are related to it in di®erent contexts by performing a community-discovery algorithm on folksonomy networks. The sets of tags are then used to disambiguate search results returned by del.icio.us and Google. Experimental results show that our method is able to disambiguate the documents returned by the two systems with high precision.

Collaborative tagging systems are becoming very popular recently. Web users use freely-chosen tags to describe shared resources, resulting in a folksonomy. One problem of folksonomies is that tags which appear in the same form may carry multiple meanings and represent different concepts. As this kind of tags are ambiguous, the precisions in both description and retrieval of the shared resources are reduced. We attempt to develop effective methods to disambiguate tags by studying the tripartite structure of folksonomies. This paper describes the network analysis techniques that we employ to discover clusters of nodes in networks and the algorithm for tag disambiguation. Experiments show that the method is very effective in performing the task.

The use of tags to describe Web resources in a collaborative manner has experienced rising popularity among Web users in recent years. The product of such activity is given the name folksonomy, which can be considered as a scheme of organizing information in the users' own way. In this paper, we present a possible way to analyze the tripartite graphs -- graphs involving users, tags and resources -- of folksonomies and discuss how these elements acquire their meanings through their associ- ations with other elements, a process we call mutual contextualization. In particular, we demonstrate how different meanings of ambiguous tags can be discovered through such analysis of the tripartite graph by study- ing the tag sf. We also discuss how the result can be used as a basis to better understand the nature of folksonomies.

The use of tags to describe Web resources in a collaborative manner has experienced rising popularity among Web users in recent years. The product of such activity is given the name folksonomy, which can be considered as a scheme of organizing information in the users’ own way. This research work attempts to analyze tripartite graphs – graphs involving users, tags and resources – of folksonomies and discuss how these elements acquire their semantics through their associations with other elements, a process we call mutual contextualization. By studying such process, we try to identify solutions to problems such as tag disambiguation, retrieving documents of similar topics and discovering communities of users. This paper describes the basis of the research work, mentions work done so far and outlines future plans.