34,561 research outputs found
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
Bridging the gap between social tagging and semantic annotation: E.D. the Entity Describer
Semantic annotation enables the development of efficient computational methods for analyzing and interacting with information, thus maximizing its value. With the already substantial and constantly expanding data generation capacity of the life sciences as well as the concomitant increase in the knowledge distributed in scientific articles, new ways to produce semantic annotations of this information are crucial. While automated techniques certainly facilitate the process, manual annotation remains the gold standard in most domains. In this manuscript, we describe a prototype mass-collaborative semantic annotation system that, by distributing the annotation workload across the broad community of biomedical researchers, may help to produce the volume of meaningful annotations needed by modern biomedical science. We present E.D., the Entity Describer, a mashup of the Connotea social tagging system, an index of semantic web-accessible controlled vocabularies, and a new public RDF database for storing social semantic annotations
Tag-Aware Recommender Systems: A State-of-the-art Survey
In the past decade, Social Tagging Systems have attracted increasing
attention from both physical and computer science communities. Besides the
underlying structure and dynamics of tagging systems, many efforts have been
addressed to unify tagging information to reveal user behaviors and
preferences, extract the latent semantic relations among items, make
recommendations, and so on. Specifically, this article summarizes recent
progress about tag-aware recommender systems, emphasizing on the contributions
from three mainstream perspectives and approaches: network-based methods,
tensor-based methods, and the topic-based methods. Finally, we outline some
other tag-related works and future challenges of tag-aware recommendation
algorithms.Comment: 19 pages, 3 figure
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
Horizon Report 2009
El informe anual Horizon investiga, identifica y clasifica las tecnologÃas emergentes que los expertos que lo elaboran prevén tendrán un impacto en la enseñanza aprendizaje, la investigación y la producción creativa en el contexto educativo de la enseñanza superior. También estudia las tendencias clave que permiten prever el uso que se hará de las mismas y los retos que ellos suponen para las aulas. Cada edición identifica seis tecnologÃas o prácticas. Dos cuyo uso se prevé emergerá en un futuro inmediato (un año o menos) dos que emergerán a medio plazo (en dos o tres años) y dos previstas a más largo plazo (5 años)
Collaborative tagging as a knowledge organisation and resource discovery tool
The purpose of the paper is to provide an overview of the collaborative tagging phenomenon and explore some of the reasons for its emergence. Design/methodology/approach - The paper reviews the related literature and discusses some of the problems associated with, and the potential of, collaborative tagging approaches for knowledge organisation and general resource discovery. A definition of controlled vocabularies is proposed and used to assess the efficacy of collaborative tagging. An exposition of the collaborative tagging model is provided and a review of the major contributions to the tagging literature is presented. Findings - There are numerous difficulties with collaborative tagging systems (e.g. low precision, lack of collocation, etc.) that originate from the absence of properties that characterise controlled vocabularies. However, such systems can not be dismissed. Librarians and information professionals have lessons to learn from the interactive and social aspects exemplified by collaborative tagging systems, as well as their success in engaging users with information management. The future co-existence of controlled vocabularies and collaborative tagging is predicted, with each appropriate for use within distinct information contexts: formal and informal. 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. Originality/value - At time of writing there were no literature reviews summarising the main contributions to the collaborative tagging research or debate
User Motives for Tagging Video Content
User tagging of video content provides many possibilities for indexing and personalization. To exploit these possibilities, users must be willing to tag the video content they watch. In this paper we present the first results of our ongoing research, by constructing an overview of user motives to tag video content. We present the results of a study in which we elicited possible user motives to tag movies on the internet. The identified motives include the categories ‘indexing’, ‘socializing’ and ‘communicating’. Finally, user barriers to tag video content are discussed
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