948 research outputs found
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 indexerâs mindset, demonstrating that folksonomies used in the del.icio.us bookmarking service are a potential source for generating semantic metadata to annotate web resources
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
The state of research on folksonomies in the field of Library and Information Science : a Systematic Literature Review
Purpose â The purpose of this thesis is to provide an overview of all relevant peer-reviewed articles on folksonomies, social tagging and social bookmarking as knowledge organisation systems within the field of Library and Information Science by reviewing the current state of research on these systems of managing knowledge. Method â I use the systematic literature review method in order to systematically and transparently review and synthesise data extracted from 39 articles found through the discovery system LUBsearch in order to find out which, and to which degree different methods, theories and systems are represented, which subfields can be distinguished, how present research within these subfields is and which larger conclusions can be drawn from research conducted between 2003-2013 on folksonomies. Findings â There have been done many studies which are exploratory or reviewing literature discussions, and other frequently used methods which have been used are questionnaires or surveys, although often in conjunction with other methods. Furthermore, out of the 39 studies, 22 were quantitative, 15 were qualitative and 2 used mixed methods. I also found that there were an underwhelming number of theories being explicitly used, where merely 11 articles explicitly used theories, and only one theory was used twice. No key authors on the topic were identified, though Knowledge Organization, Information Processing & Management and Journal of the American Society for Information Science and Technology were recognised as key journals for research on folksonomies. There have been plenty of studies on how tags and folksonomies have effected other knowledge organisation systems, or how pre-existing have been used to create new systems. Other well represented subfields include studies on the quality or characteristics of tags or text, and studies aiming to improve folksonomies, search methods or tags. Value â I provide an overview on what has been researched and where the focus on said research has been during the last decade and present future research suggestions and identify possible dangers to be wary of which I argue will benefit folksonomies and knowledge organisation as a whole
Identifying experts and authoritative documents in social bookmarking systems
Social bookmarking systems allow people to create pointers to Web resources in a shared, Web-based environment. These services allow users to add free-text labels, or âtagsâ, to their bookmarks as a way to organize resources for later recall. Ease-of-use, low cognitive barriers, and a lack of controlled vocabulary have allowed social bookmaking systems to grow exponentially over time. However, these same characteristics also raise concerns. Tags lack the formality of traditional classificatory metadata and suffer from the same vocabulary problems as full-text search engines. It is unclear how many valuable resources are untagged or tagged with noisy, irrelevant tags. With few restrictions to entry, annotation spamming adds noise to public social bookmarking systems. Furthermore, many algorithms for discovering semantic relations among tags do not scale to the Web.
Recognizing these problems, we develop a novel graph-based Expert and Authoritative Resource Location (EARL) algorithm to find the most authoritative documents and expert users on a given topic in a social bookmarking system. In EARLâs first phase, we reduce noise in a Delicious dataset by isolating a smaller sub-network of âcandidate expertsâ, users whose tagging behavior shows potential domain and classification expertise. In the second phase, a HITS-based graph analysis is performed on the candidate expertsâ data to rank the top experts and authoritative documents by topic. To identify topics of interest in Delicious, we develop a distributed method to find subsets of frequently co-occurring tags shared by many candidate experts.
We evaluated EARLâs ability to locate authoritative resources and domain experts in Delicious by conducting two independent experiments. The first experiment relies on human judgesâ n-point scale ratings of resources suggested by three graph-based algorithms and Google. The second experiment evaluated the proposed approachâs ability to identify classification expertise through human judgesâ n-point scale ratings of classification terms versus expert-generated data
Exploring Characteristics of Social Classification
Three empirical studies on characteristics of social classification are reported in this paper. The first study compared social tags with controlled vocabularies and title-based automatic indexing and found little overlaps among the three indexing methods. The second study investigated how well tags could be categorized to improve effectiveness of searching and browsing. The third study explored factors and radios that had the most significant impact on tag convergence. Finding of the three studies will help to identify characteristics of those tagging terms that are content-rich and that can be used to increase effectiveness of tagging, searching and browsing
Searching with Tags: Do Tags Help Users Find Things?
This study examines the question of whether tags can be useful in the process of information retrieval. Participants searched a social bookmarking tool specialising in academic articles (CiteULike) and an online journal database (Pubmed). Participant actions were captured using screen capture software and they were asked to describe their search process. Users did make use of tags in their search process, as a guide to searching and as hyperlinks to potentially useful articles. However, users also made use of controlled vocabularies in the journal database to locate useful search terms and of links to related articles supplied by the database
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
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
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