6 research outputs found
Exploiting Social Annotation for Automatic Resource Discovery
Information integration applications, such as mediators or mashups, that
require access to information resources currently rely on users manually
discovering and integrating them in the application. Manual resource discovery
is a slow process, requiring the user to sift through results obtained via
keyword-based search. Although search methods have advanced to include evidence
from document contents, its metadata and the contents and link structure of the
referring pages, they still do not adequately cover information sources --
often called ``the hidden Web''-- that dynamically generate documents in
response to a query. The recently popular social bookmarking sites, which allow
users to annotate and share metadata about various information sources, provide
rich evidence for resource discovery. In this paper, we describe a
probabilistic model of the user annotation process in a social bookmarking
system del.icio.us. We then use the model to automatically find resources
relevant to a particular information domain. Our experimental results on data
obtained from \emph{del.icio.us} show this approach as a promising method for
helping automate the resource discovery task.Comment: 6 pages, submitted to AAAI07 workshop on Information Integration on
the We
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
Community-Oriented Models and Applications for the Social Web
The past few years have seen the rapid rise of all things "social" on the web from the growth of online social networks like Facebook, to user-contributed content sites
like Flickr and YouTube, to social bookmarking services like Delicious, among many others. Whereas traditional approaches to organizing and accessing the web’s massive
amount of information have focused on content-based and link-based approaches, these social systems offer rich opportunities for user-based and community-based exploration and analysis of the web by building on the unprecedented access to the interests and perspectives of millions of users.
We focus here on the challenge of modeling and mining social bookmarking systems, in which resources are enriched by large-scale socially generated metadata
(“tags”) and contextualized by the user communities that are associated with the resources. Our hypothesis is that an underlying social collective intelligence is embedded
in the uncoordinated actions of users on social bookmarking services, and that this social collective intelligence can be leveraged for enhanced web-based information discovery and knowledge sharing. Concretely, we posit the existence of underlying implicit communities in these social bookmarking systems that drive the social bookmarking process which can provide a foundation for community-based organization of web resources.
To that end, we make three contributions:
• First, we propose a pair of novel probabilistic generative models for describing and modeling community-oriented social bookmarking. We show how these
models enable effective extraction of meaningful communities over large real world social bookmarking services.
• Second, we develop two frameworks for community-based web information browsing and search that are based on these community-oriented social bookmarking models. We show how both achieve improved discovery and exploration of the social web.
• Third, we introduce a community evolution framework for studying and analyzing social bookmarking communities over time. We explore the temporal dimension of social bookmarking and explore the dynamics of community formation, evolution, and dissolution. By uncovering implicit communities, putting them to use in an application scenario (search and browsing), and analyzing them over time, this dissertation provides a foundation for the study of how social knowledge networks are self-organized, a deeper understanding and appreciation of the factors impacting collective intelligence, and the creation of new information access algorithms for leveraging these communities
Using Data Mining for Facilitating User Contributions in the Social Semantic Web
This thesis utilizes recommender systems to aid the user in contributing to the Social Semantic Web. In this work, we propose a framework that maps domain properties to recommendation technologies. Next, we develop novel recommendation algorithms for improving personalized tag recommendation and for recommendation of semantic relations. Finally, we introduce a framework to analyze different types of potential attacks against social tagging systems and evaluate their impact on those systems