1,479 research outputs found
Folksonomies and clustering in the collaborative system CiteULike
We analyze CiteULike, an online collaborative tagging system where users
bookmark and annotate scientific papers. Such a system can be naturally
represented as a tripartite graph whose nodes represent papers, users and tags
connected by individual tag assignments. The semantics of tags is studied here,
in order to uncover the hidden relationships between tags. We find that the
clustering coefficient reflects the semantical patterns among tags, providing
useful ideas for the designing of more efficient methods of data classification
and spam detection.Comment: 9 pages, 5 figures, iop style; corrected typo
Measuring Similarity in Large-Scale Folksonomies
Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites. Unlike\ud
taxonomies, which overimpose a hierarchical categorisation of content, folksonomies enable end-users to freely create and choose the categories (in this case, tags) that best\ud
describe some content. However, as tags are informally de-\ud
fined, continually changing, and ungoverned, social tagging\ud
has often been criticised for lowering, rather than increasing, the efficiency of searching, due to the number of synonyms, homonyms, polysemy, as well as the heterogeneity of\ud
users and the noise they introduce. To address this issue, a\ud
variety of approaches have been proposed that recommend\ud
users what tags to use, both when labelling and when looking for resources. As we illustrate in this paper, real world\ud
folksonomies are characterized by power law distributions\ud
of tags, over which commonly used similarity metrics, including the Jaccard coefficient and the cosine similarity, fail\ud
to compute. We thus propose a novel metric, specifically\ud
developed to capture similarity in large-scale folksonomies,\ud
that is based on a mutual reinforcement principle: that is,\ud
two tags are deemed similar if they have been associated to\ud
similar resources, and vice-versa two resources are deemed\ud
similar if they have been labelled by similar tags. We offer an efficient realisation of this similarity metric, and assess its quality experimentally, by comparing it against cosine similarity, on three large-scale datasets, namely Bibsonomy, MovieLens and CiteULike
Exploring The Value Of Folksonomies For Creating Semantic Metadata
Finding good keywords to describe resources is an on-going problem: typically we select such words manually from a thesaurus of terms, or they are created using automatic keyword extraction techniques. Folksonomies are an increasingly well populated source of unstructured tags describing web resources. This paper explores the value of the folksonomy tags as potential source of keyword metadata by examining the relationship between folksonomies, community produced annotations, and keywords extracted by machines. The experiment has been carried-out in two ways: subjectively, by asking two human indexers to evaluate the quality of the generated keywords from both systems; and automatically, by measuring the percentage of overlap between the folksonomy set and machine generated keywords set. The results of this experiment show that the folksonomy tags agree more closely with the human generated keywords than those automatically generated. The results also showed that the trained indexers preferred the semantics of folksonomy tags compared to keywords extracted automatically. These results can be considered as evidence for the strong relationship of folksonomies to the human indexer’s mindset, demonstrating that folksonomies used in the del.icio.us bookmarking service are a potential source for generating semantic metadata to annotate web resources
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
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
Hypergraph model of social tagging networks
The past few years have witnessed the great success of a new family of
paradigms, so-called folksonomy, which allows users to freely associate tags to
resources and efficiently manage them. In order to uncover the underlying
structures and user behaviors in folksonomy, in this paper, we propose an
evolutionary hypergrah model to explain the emerging statistical properties.
The present model introduces a novel mechanism that one can not only assign
tags to resources, but also retrieve resources via collaborative tags. We then
compare the model with a real-world dataset: \emph{Del.icio.us}. Indeed, the
present model shows considerable agreement with the empirical data in following
aspects: power-law hyperdegree distributions, negtive correlation between
clustering coefficients and hyperdegrees, and small average distances.
Furthermore, the model indicates that most tagging behaviors are motivated by
labeling tags to resources, and tags play a significant role in effectively
retrieving interesting resources and making acquaintance with congenial
friends. The proposed model may shed some light on the in-depth understanding
of the structure and function of folksonomy.Comment: 7 pages,7 figures, 32 reference
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
Bridging the gap between folksonomies and the semantic web: an experience report
Abstract. While folksonomies allow tagging of similar resources with a variety of tags, their content retrieval mechanisms are severely hampered by being agnostic to the relations that exist between these tags. To overcome this limitation, several methods have been proposed to find groups of implicitly inter-related tags. We believe that content retrieval can be further improved by making the relations between tags explicit. In this paper we propose the semantic enrichment of folksonomy tags with explicit relations by harvesting the Semantic Web, i.e., dynamically selecting and combining relevant bits of knowledge from online ontologies. Our experimental results show that, while semantic enrichment needs to be aware of the particular characteristics of folksonomies and the Semantic Web, it is beneficial for both.
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