A scalable mining of frequent quadratic concepts in d-folksonomies

Folksonomy mining is grasping the interest of web 2.0 community since it represents the core data of social resource sharing systems. However, a scrutiny of the related works interested in mining folksonomies unveils that the time stamp dimension has not been considered. For example, the wealthy number of works dedicated to mining tri-concepts from folksonomies did not take into account time dimension. In this paper, we will consider a folksonomy commonly composed of triples and we shall consider the time as a new dimension. We motivate our approach by highlighting the battery of potential applications. Then, we present the foundations for mining quadri-concepts, provide a formal definition of the problem and introduce a new efficient algorithm, called QUADRICONS for its solution to allow for mining folksonomies in time, i.e., d-folksonomies. We also introduce a new closure operator that splits the induced search space into equivalence classes whose smallest elements are the quadri-minimal generators. Carried out experiments on large-scale real-world datasets highlight good performances of our algorithm

Tagging, Folksonomy & Co - Renaissance of Manual Indexing?

This paper gives an overview of current trends in manual indexing on the Web. Along with a general rise of user generated content there are more and more tagging systems that allow users to annotate digital resources with tags (keywords) and share their annotations with other users. Tagging is frequently seen in contrast to traditional knowledge organization systems or as something completely new. This paper shows that tagging should better be seen as a popular form of manual indexing on the Web. Difference between controlled and free indexing blurs with sufficient feedback mechanisms. A revised typology of tagging systems is presented that includes different user roles and knowledge organization systems with hierarchical relationships and vocabulary control. A detailed bibliography of current research in collaborative tagging is included.Comment: Preprint. 12 pages, 1 figure, 54 reference

Social bookmarking: an empirical analysis of connotea users’ perspectives

This study examined the perspectives of experienced and inexperienced users of Connotea in terms of reported bookmarking behaviors, perceived usefulness of social bookmarking in information discovery and management, and perceived usefulness of particular Connotea features. A convenience sample of experienced (n=30) and inexperienced users (n=32) responded to an online survey. The questionnaire utilized a 4-point Likert scale to examine the respondents’ opinions. The findings showed that both experienced and inexperienced users of Connotea perceived social bookmarking to be useful for information discovery and management. They also perceived the features and policies of Connotea to be useful for their personal purposes. However, the reported frequencies of usage indicated that the extent of use of social bookmarking may not be substantial. Experienced users were also found to use social bookmarking for managing relevant websites while inexperienced users still preferred to use traditional bookmarking in dedicated computers. These findings have potential implications on the development and use of social bookmarking services. Through our results, we provided information on the human factors that may be considered for further improvement of social bookmarking applications.published_or_final_versionThe 2010 CITE Research Symposium on 'e-Learning Design and Designs for Learning', the University of Hong Kong, Hong Kong, 4-6 March 2010

Tags Are Related: Measurement of Semantic Relatedness Based on Folksonomy Network

Folksonomy and tagging systems, which allow users to interactively annotate a pool of shared resources using descriptive tags, have enjoyed phenomenal success in recent years. The concepts are organized as a map in human mind, however, the tags in folksonomy, which reflect users' collaborative cognition on information, are isolated with current approach. What we do in this paper is to estimate the semantic relatedness among tags in folksonomy: whether tags are related from semantic view, rather than isolated? We introduce different algorithms to form networks of folksonomy, connecting tags by users collaborative tagging, or by resource context. Then we perform multiple measures of semantic relatedness on folksonomy networks to investigate semantic information within them. The result shows that the connections between tags have relatively strong semantic relatedness, and the relatedness decreases dramatically as the distance between tags increases. What we find in this paper could provide useful visions in designing future folksonomy-based systems, constructing semantic web in current state of the Internet, and developing natural language processing applications

Changing Higher Education Learning with Web 2.0 and Open Education Citation, Annotation, and Thematic Coding Appendices

Appendices of citations, annotations and themes for research conducted on four websites: Delicious, Wikipedia, YouTube, and Facebook

Content-awareness and graph-based ranking for tag recommendation in folksonomies

Tag recommendation algorithms aid the social tagging process in many userdriven document indexing applications, such as social bookmarking and publication sharing websites. This thesis gives an overview of existing tag recommendation methods and proposes novel approaches that address the new document problem and the task of ranking tags. The focus is on graph-based methods such as Folk- Rank that apply weight spreading algorithms to a graph representation of the folksonomy. In order to suggest tags for previously untagged documents, extensions are presented that introduce content into the recommendation process as an additional information source. To address the problem of ranking tags, an in-depth analysis of graph models as well as ranking algorithms is conducted. Implicit assumptions made by the widely-used graph model of the folksonomy are highlighted and an improved model is proposed that captures the characteristics of the social tagging data more accurately. Additionally, issues in the tag rank computation of FolkRank are analysed and an adapted weight spreading approach for social tagging data is presented. Moreover, the applicability of conventional weight spreading methods to data from the social tagging domain is examined in detail. Finally, indications of implicit negative feedback in the data structure of folksonomies are analysed and novel approaches of identifying negative relationships are presented. By exploiting the three-dimensional characteristics of social tagging data the proposed metrics are based on stronger evidence and provide reliable measures of negative feedback. Including content into the tag recommendation process leads to a significant increase in recommendation accuracy on real-world datasets. The proposed adaptations to graph models and ranking algorithms result in more accurate and computationally less expensive recommenders. Moreover, new insights into the fundamental characteristics of social tagging data are revealed and a novel data interpretation that takes negative feedback into account is proposed

Personalized Recommendations Based On Users’ Information-Centered Social Networks

The overwhelming amount of information available today makes it difficult for users to find useful information and as the solution to this information glut problem, recommendation technologies emerged. Among the several streams of related research, one important evolution in technology is to generate recommendations based on users’ own social networks. The idea to take advantage of users’ social networks as a foundation for their personalized recommendations evolved from an Internet trend that is too important to neglect – the explosive growth of online social networks. In spite of the widely available and diversified assortment of online social networks, most recent social network-based recommendations have concentrated on limited kinds of online sociality (i.e., trust-based networks and online friendships). Thus, this study tried to prove the expandability of social network-based recommendations to more diverse and less focused social networks. The online social networks considered in this dissertation include: 1) a watching network, 2) a group membership, and 3) an academic collaboration network. Specifically, this dissertation aims to check the value of users’ various online social connections as information sources and to explore how to include them as a foundation for personalized recommendations. In our results, users in online social networks shared similar interests with their social partners. An in-depth analysis about the shared interests indicated that online social networks have significant value as a useful information source. Through the recommendations generated by the preferences of social connection, the feasibility of users’ social connections as a useful information source was also investigated comprehensively. The social network-based recommendations produced as good as, or sometimes better, suggestions than traditional collaborative filtering recommendations. Social network-based recommendations were also a good solution for the cold-start user problem. Therefore, in order for cold-start users to receive reasonably good recommendations, it is more effective to be socially associated with other users, rather than collecting a few more items. To conclude, this study demonstrates the viability of multiple social networks as a means for gathering useful information and addresses how different social networks of a novelty value can improve upon conventional personalization technology

Generation of Classificatory Metadata for Web Resources using Social Tags

With the increasing popularity of social tagging systems, the potential for using social tags as a source of metadata is being explored. Social tagging systems can simplify the involvement of a large number of users and improve the metadata generation process, especially for semantic metadata. This research aims to find a method to categorize web resources using social tags as metadata. In this research, social tagging systems are a mechanism to allow non-professional catalogers to participate in metadata generation. Because social tags are not from a controlled vocabulary, there are issues that have to be addressed in finding quality terms to represent the content of a resource. This research examines ways to deal with those issues to obtain a set of tags representing the resource from the tags provided by users.Two measurements that measure the importance of a tag are introduced. Annotation Dominance (AD) is a measurement of how much a tag term is agreed to by users. Another is Cross Resources Annotation Discrimination (CRAD), a measurement to discriminate tags in the collection. It is designed to remove tags that are used broadly or narrowly in the collection. Further, the study suggests a process to identify and to manage compound tags. The research aims to select important annotations (meta-terms) and remove meaningless ones (noise) from the tag set. This study, therefore, suggests two main measurements for getting a subset of tags with classification potential. To evaluate the proposed approach to find classificatory metadata candidates, we rely on users' relevance judgments comparing suggested tag terms and expert metadata terms. Human judges rate how relevant each term is on an n-point scale based on the relevance of each of the terms for the given resource