We present the design of a new social bookmark manager, named GalViz, as part of the interface of the GiveA-Link system. Unlike the interfaces of traditional social tagging tools, which usually display information in a list view, GalViz visualizes tags, resources, social links, and social context in an interactive network, combined with the tag cloud. Evaluations through a scenario case study and log analysis provide evidence of the effectiveness of our design.
There is sparse literature on the design principles of social tagging tools @cite_18 @cite_21 @cite_2 . Some work has been done on building hierarchy structure from the social tagging to uncover the hidden child-parent semantics @cite_6 @cite_14 . Visualization, as a powerful type of social bookmarking tool, has been studied and utilized in several existing designs. Visualization of hyperlinks between Web pages was adopted to enhance adaptive navigation @cite_8 . Cluster Map, a social bookmark visualization tool, highlighted the relationships among users and bookmarks to identify tag and community structures @cite_4 . Unlike ClusterMap, is designed to emphasize the semantic relationships between tags and resources to help users manage existing resources and discover new ones. Besides, the graphical interface was shown to be useful for distributed collaborations and interactions on social bookmarking @cite_13 . Graphical visualization of concept networks was integrated into several Web applications as an innovative interactive user interface @cite_17 @cite_11 . Most of such existing applications display context of items of the same type, but is able to provide the visualization of heterogenous networks among two different objects, tags and resources.
@cite_18 @cite_14 @cite_4 @cite_11 @cite_8 @cite_21 @cite_6 @cite_2 @cite_13 @cite_17
1560880555 2152019382 2078425114 204790106
Social bookmarking tools are very popular nowadays. In most tools, users tag the bookmarks to describe them. Therefore, it is of- ten hard for users to discover implicit structures between tags, users and bookmarks. We think that this is essential for both end users to discover new bookmarks that could be of interest to them, and for researchers who want to study how people use social information re- trieval tools. In this work, a cluster map visualisation technique is customized to enable users to explore social bookmarks in the del.icio.us and the CALIBRATE system. The design of our visualisation aims to automatically identify tag and community structures, and visualises these structures in order to increase the users awareness of them. Social bookmarking systems are becoming increasingly important data sources for bootstrapping and maintaining Semantic Web applications. Their emergent information structures have become known as folksonomies. A key question for harvesting semantics from these systems is how to extend and adapt traditional notions of similarity to folksonomies, and which measures are best suited for applications such as community detection, navigation support, semantic search, user profiling and ontology learning. Here we build an evaluation framework to compare various general folksonomy-based similarity measures, which are derived from several established information-theoretic, statistical, and practical measures. Our framework deals generally and symmetrically with users, tags, and resources. For evaluation purposes we focus on similarity between tags and between resources and consider different methods to aggregate annotations across users. After comparing the ability of several tag similarity measures to predict user-created tag relations, we provide an external grounding by user-validated semantic proxies based on WordNet and the Open Directory Project. We also investigate the issue of scalability. We find that mutual information with distributional micro-aggregation across users yields the highest accuracy, but is not scalable; per-user projection with collaborative aggregation provides the best scalable approach via incremental computations. The results are consistent across resource and tag similarity. In the era of big data, collaborative tagging (a.k.a. folksonomy) systems have proliferated as a consequence of the growth of Web 2.0 communities. Constructing user profiles from folksonomy systems is useful for many applications such as personalized search and recommender systems. The identification of latent user communities is one way to better understand and meet user needs. The behavior of users is highly influenced by the behavior of their neighbors or community members, and this can be utilized in constructing user profiles. However, conventional user profiling techniques often encounter data sparsity problems as data from a single user is insufficient to build a powerful profile. Hence, in this paper we propose a method of enriching user profiles based on latent user communities in folksonomy data. Specifically, the proposed approach contains four sub-processes: (i) tag-based user profiles are extracted from a folksonomy tripartite graph; (ii) a multi-faceted folksonomy graph is constructed by integrating tag and image affinity subgraphs with the folksonomy tripartite graph; (iii) random walk distance is used to unify various relationships and measure user similarities; (iv) a novel prototype-based clustering method based on user similarities is used to identify user communities, which are further used to enrich the extracted user profiles. To evaluate the proposed method, we conducted experiments using a public dataset, the results of which show that our approach outperforms previous ones in user profile enrichment. Tagging-based systems enable users to categorize web resources by means of tags (freely chosen keywords), in order to refinding these resources later. Tagging is implicitly also a social indexing process, since users share their tags and resources, constructing a social tag index, so-called folksonomy. At the same time of tagging-based system, has been popularised an interface model for visual information retrieval known as Tag-Cloud. In this model, the most frequently used tags are displayed in alphabetical order. This paper presents a novel approach to Tag-Cloud’s tags selection, and proposes the use of clustering algorithms for visual layout, with the aim of improve browsing experience. The results suggest that presented approach reduces the semantic density of tag set, and improves the visual consistency of Tag-Cloud layout.