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The panel will explore the relevance of the emerging tagging systems (Flickr, Del.icio.us, RawSugar and more). Why do they seem to work? What kinds of incentives are required for users to participate? Will tagging survive and scale to mass adoption? What are the behavioral, economic, and social models that underlie each tagging system? What are the dynamics of those systems, and how are they derived from the specific application's design and affordances?.We will demand answers to these questions and others from some of the pioneering practitioners and academics in the field. Bring your wireless laptop to participate in a live tagging experiment! The experiment results will be shown and discussed at the end of the panel. To add to the fun, parts of the discussion will be motivated by short video segments.
In this paper we explore a method of decomposition of compound tags found in social tagging systems
and outline several results, including improvement of search indexes, extraction of semantic information,
and benefits to usability. Analysis of tagging habits demonstrates that social tagging systems such as
del.icio.us and flickr include both formal metadata, such as geotags, and informally created metadata,
such as annotations and descriptions. The majority of tags represent informal metadata; that is, they are
not structured according to a formal model, nor do they correspond to a formal ontology.
Statistical exploration of the main tag corpus demonstrates that such searches use only a subset of the
available tags; for example, many tags are composed as ad hoc compounds of terms. In order to improve
accuracy of searching across the data contained within these tags, a method must be employed to
decompose compounds in such a way that there is a high degree of confidence in the result. An approach
to decomposition of English-language compounds, designed for use within a small initial sample tagset, is
described. Possible decompositions are identified from a generous wordlist, subject to selective lexicon
snipping. In order to identify the most likely, a Bayesian classifier is used across term elements. To
compensate for the limited sample set, a word classifier is employed and the results classified using a
similar method, resulting in a successful classification rate of 88%, and a false negative rate of only 1%.
Research limitations/implications – Librarians and information professional researchers should be playing a leading role in research aimed at assessing the efficacy of collaborative tagging in relation to information storage, organisation, and retrieval, and to influence the future development of collaborative tagging systems.
Practical implications – The paper indicates clear areas where digital libraries and repositories could innovate in order to better engage users with information.
Collaborative tagging systems, or folksonomies, have the potential of becoming technological infrastructure to support knowledge management activities in an organization or a society. There are many challenges, however. This paper presents designs that enhance collaborative tagging systems to meet some key challenges: community identification, ontology generation, user and document recommendation. Design prototypes, evaluation methodology and selected preliminary results are presented.