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.

Collaborative, social tagging and annotation systems have exploded on the Internet as part of the Web 2.0 phenomenon. Systems such as Flickr, Del.icio.us, Technorati, Connotea and LibraryThing, provide a community-driven approach to classifying information and resources on the Web, so that they can be browsed, discovered and re-used. Although social tagging sites provide simple, user-relevant tags, there are issues associated with the quality of the metadata and the scalability compared with conventional indexing systems. In this paper we propose a hybrid approach that enables authoritative metadata generated by traditional cataloguing methods to be merged with community annotations and tags. The HarvANA (Harvesting and Aggregating Networked Annotations) system uses a standardized but extensible RDF model for representing the annotations/tags and OAI-PMH to harvest the annotations/tags from distributed community servers. The harvested annotations are aggregated with the authoritative metadata in a centralized metadata store. This streamlined, interoperable, scalable approach enables libraries, archives and repositories to leverage community enthusiasm for tagging and annotation, augment their metadata and enhance their discovery services. This paper describes the HarvANA system and its evaluation through a collaborative testbed with the National Library of Australia using architectural images from PictureAustralia.

Weblogs, or blogs are radically changing the face of communication within enterprises. While at the minimum blogs empower employees to publicly voice opinion and share expertise, collectively they improve collaboration and enable internal business intelligence. Though the power of blogs within organizations is well accepted, their properties, structure and utility has not yet been formally analyzed. In this paper, we study the use of blogs within a large corporation to reveal some of the interesting characteristics. We propose new techniques to model the reach and impact of posts using the corporate hierarchy. We discuss how such a technique can feed into tools that identify the reach of blog posts, and the emergence of trends and experts within an organization. 1.

Folksonomies, collaboratively created sets of metadata, are becoming more and more important for organising information and knowledge of communites in the Web. While for a single user the difference to keyword assignment is marginal, the power of folksonomies emerges from the collaborative aspects. Folksonomies are already issue of research. Within this publication we analyse underlying statistical properties of broad folksonomies aiming to identify laws and characteristics, which allow inferring properties for folksonomy based retrieval. The actual benefit of folksonomies for retrieval and the derived methods are concluded from experiments with aggregated data from del.icio.us 1. 1

Ontologies and tagging systems are two different ways to organize the knowledge present in Web. The first one has a formal fundamental that derives from descriptive logic and artificial intelligence. The other one is simpler and it integrates heterogeneous contents, and it is based on the collaboration of users in the Web 2.0. In this paper we propose a method to model tagging systems like folksonomies using ontologies. In our proposal, structured information (ontologies) can be extracted from knowledge built in a simple and collaborative way (folksonomies). Furthermore, we provide an analytical expression to evaluate the system requirements to store the derived ontology.

This paper outlines the work of the UK National Centre for e-Social Science and its plans for facilitating the take-up of Grid infrastructure and tools within the social science research community. It describes the kinds of social science research challenges to which Grid technologies and tools have been applied, the contribution that NCeSS can make to support the wider take-up of e-Science and, finally, its plans for future work. 1.

The lack of semantic metadata is becoming a barrier for the in-depth study and wide adoption of Semantic Web. At the same time, folksonomy draws more and more attention as a promising source of semantic metadata. By avoiding the use of ”apriori” agreements on ontology, which is the main feature for folksonomy, the interoperations among metadata from different users could be likely supported by dynamically constructed emergent semantics. In this paper the authors propose a concept model that supports metadata generation by extending the ideas from folksonomy. A semantic layer is specified in the model that comprises three types of semantics varying from simple to complex to support different kinds of semantic interoperations. An implementation of the model, Lego-Note, the folksonomy system featured by graphic tagging is introduced. The preliminary experiment is performed as a case study, and the qualitative analysis on social consensus discovery is exhibited. 1

Social tagging systems have recently developed as a popular method of data organisation on the Internet. These systems allow users to organise their content in a way that makes sense to them, rather than forcing them to use a pre-determined and rigid set of categorisations. These “folksonomies ” provide well populated sources of unstructured tags describing web resources which could potentially be used as semantic index terms for these resources. However getting people to agree on what tags best describe a resource is a difficult problem, therefore any feature which increases the consistency and stability of terms chosen would be extremely beneficial. We investigate how the provision of a tag cloud, a weighted list of terms commonly used to assist in browsing a folksonomy, during the tagging process itself influences the tags produced and how difficult the user perceived the task to be. We show that illustrating the most popular tags to users assists in the tagging process and encourages a stable and consistent folksonomy to form.

Abstract. Semantic technologies are very helpful in improving existing systems for searching, managing and retrieving of resources, e.g. image search, bookmarking or expert finder systems. They enhance these systems through background knowledge stored in ontologies. However, in most cases, resources in these systems change very fast. In consequence, they require a dynamic and agile change of underlying ontologies. Also, the formality of these ontologies must fit the users needs and capabilities and must be appropriate and usable. Therefore, a continuous, collaborative and work or task integrated development of these ontologies is required. In this paper, we present how these requirements occur in real world applications and how they are solved and implemented using our Ontology Maturing Process Model. 1

To utilize the dramatically increasing Blog contents and widely accepted web 2.0 services, this research proposes myFOAF which extends FOAF to model dynamic social contexts over the web. Based on the richer social semantic, we can generate the blogger’s myFOAF profile, via the corresponding contents such as RSS, Blog content, tagging data, mashup service that retrieve from blogger’s daily web behavior. Two main application models are addressed in the system. Firstly, the Personal Expertise Explorer can accumulate blogger’s tagging ontology (myTag) to analyze the blogger’s domain related expertise. The evaluation result shows that the average precision reaches around 77.6%. Secondly, the Personal Social Explorer gathers the blogger’s friend information to form a personalized social network diagram which presents blogger’s social activities in certain tag domain. With the extra semantic supported by myFOAF, blogger can easily explore his own implicit expertise and web social relationship with minimal efforts.