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The purpose of the paper is to provide an overview of the collaborative tagging phenomenon and explore some of the reasons for its emergence.

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.

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.

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.

The paper indicates clear areas where digital libraries and repositories could innovate in order to better engage users with information.

At time of writing there were no literature reviews summarising the main contributions to the collaborative tagging research or debate.

The purpose of this paper is to investigate the linking of a folksonomy (user vocabulary) and LCSH (controlled vocabulary) on the basis of word matching, for the potential use of LCSH in bringing order to folksonomies.

A selected sample of a folksonomy from a popular collaborative tagging system, Delicious, was word‐matched with LCSH. LCSH was transformed into a tree structure called an LCSH tree for the matching. A close examination was conducted on the characteristics of folksonomies, the overlap of folksonomies with LCSH, and the distribution of folksonomies over the LCSH tree.

The experimental results showed that the total proportion of tags being matched with LC subject headings constituted approximately two‐thirds of all tags involved, with an additional 10 percent of the remaining tags having potential matches. A number of barriers for the linking as well as two areas in need of improving the matching are identified and described. Three important tag distribution patterns over the LCSH tree were identified and supported: skewedness, multifacet, and Zipfian‐pattern.

The results of the study can be adopted for the development of innovative methods of mapping between folksonomy and LCSH, which directly contributes to effective access and retrieval of tagged web resources and to the integration of multiple information repositories based on the two vocabularies.

The linking of controlled vocabularies can be applicable to enhance information retrieval capability within collaborative tagging systems as well as across various tagging system information depositories and bibliographic databases.

This is among frontier works that examines the potential of linking a folksonomy, extracted from a collaborative tagging system, to an authority‐maintained subject heading system. It provides exploratory data to support further advanced mapping methods for linking the two vocabularies.

Video summarisation is one of the most active fields in content‐based video retrieval research. A new video summarisation scheme is proposed by this paper based on socially generated temporal tags.

To capture users' collaborative tagging activities the proposed scheme maintains video bookmarks, which contain some temporal or positional information about videos, such as relative time codes or byte offsets. For each video all the video bookmarks collected from users are then statistically analysed in order to extract some meaningful key frames (the video equivalent of keywords), which collectively constitute the summary of the video.

Compared with traditional video summarisation methods that use low‐level audio‐visual features, the proposed method is based on users' high‐level collaborative activities, and thus can produce semantically more important summaries than existing methods.

It is assumed that the video frames around the bookmarks inserted by users are informative and representative, and therefore can be used as good sources for summarising videos.

Folksonomy, commonly called collaborative tagging, is a Web 2.0 method for users to freely annotate shared information resources with keywords. It has mostly been used for collaboratively tagging photos (Flickr), web site bookmarks (Del.icio.us), or blog posts (Technorati), but has never been applied to the field of automatic video summarisation. It is believed that this is the first attempt to utilise users' high‐level collaborative tagging activities, instead of low‐level audio‐visual features, for video summarisation.

This paper aims to discuss how collaborative classification works in online music information retrieval systems and its impacts on the construction, fixation and orientation of the social uses of popular music on the internet.

Using a comparative method, the paper examines the logic behind music classification in Recommender Systems by studying the case of Last.fm, one of the most popular web sites of this type on the web. Data collected about users' ritual classifications are compared with the classification used by the music industry, represented by the AllMusic web site.

The paper identifies the differences between the criteria used for the collaborative classification of popular music, which is defined by users, and the traditional standards of commercial classification, used by the cultural industries, and discusses why commercial and non‐commercial classification methods vary.

Collaborative ritual classification reveals a shift in the demand for cultural information that may affect the way in which this demand is organized, as well as the classification criteria for works on the digital music market.

Collective creation of a music classification in recommender systems represents a new model of cultural mediation that might change the way of building new uses, tastes and patterns of musical consumption in online environments.

The paper highlights the way in which the classification process might influence the behavior of the users of music information retrieval systems, and vice versa.

The purpose of this paper is to develop a general framework that incorporates collaborative social tagging with a novel ontology scheme conveying multiple perspectives.

This paper proposes a framework where multiple users tag the same object (an image in this case) and an ontology is extended based on these tags while being tolerant of different points of view. Both the tagging and the ontological models are intentionally designed to suit the multi‐perspective environment. The paper develops a method based on a set of rules that determine how to associate new concepts to predefined perspectives (in addition to determining relations to topics or other concepts as typically done in previous research) and how to insert and maintain multiple perspectives.

This case study experiment, with a set of selected annotated images, indicates the soundness of the proposed ontological model.

The proposed framework characterises the underlying processes for controlled collaborative development of a multi‐perspective ontology and its application to improve image annotation, searching and browsing. The significance of this research is that it focuses on exploring the impact of creating a constantly evolving ontology based on collaborative tagging. The paper is not aware of any other work that has attempted to devise such an environment and to study its dynamics.

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

The purpose of this paper is to use ideas drawn from two founders of American pragmatism, William James and Charles Sanders Peirce, in order to propose a philosophical foundation that supports the value of collaborative tagging and reinforces the structure and goals of the Semantic Web.

The study employs a close analysis of key literature by James and Peirce to answer recent calls for a philosophy of the Web and to respond to research in the LIS literature that has assessed the value and limitations of folksonomy. Moreover, pragmatic views are applied to illustrate the relationships among collaborative tagging, linked data, and the Semantic Web.

With a philosophical foundation in place, the study highlights the value of the minority tags that fall within the so-called “long tail” of the power law graph, and the importance of granting sufficient time for the full value of folksonomy to be revealed. The discussion goes further to explore how “collaborative tagging” could evolve into “collaborative knowledge” in the form of linked data. Specifically, Peirce’s triadic architectonic is shown to foster an understanding of the construction of linked data through the functional requirements for bibliographic records entity-relation model and resource description framework triples, and James’s image of the multiverse anticipates the goals Tim Berners-Lee has articulated for the Semantic Web.

This study is unique in using Jamesian and Peircean thinking to argue for the value of folksonomy and to suggest implications for the Semantic Web.

The purpose of this study is to examine the growth patterns of tag vocabulary in collaborative tagging systems to verify the sustainability and stabilization of tag distributions. Both sustainability and stabilization are essential to the mining and categorization of information driven by tagging behaviors.

The study was based on time series data of CiteULike from November 2004 to April 2010. Power law distributions were detected to reveal statistical regularities and tagging patterns. Logistic regression analysis with time‐dependent covariates was conducted to identify the factors affecting the growth of distinct tags for articles. The significance of the effects and the time taken for a given article to reach its tagging maturity were also explored.

Time series plots and trend analysis illustrated the continuous growth of the tagging system. Exploratory analysis of power law distribution fittings indicated a sign of system stability known as scale invariance. Logistic regression results demonstrated that for a particular article, the number of users who tagged the article, the initial date when the article was tagged, and the life span of the article are statistically significant to the ratio of the distinct tag number to the total tag number for a given article. These results confirmed that the distinct tag ratio of an article gives rise to a stable pattern.

Though extensive work has been done on the patterns of tag vocabulary, it is not clear how the growth of distinctive tags behaves in relation to the total number of tag applications, considering time‐dependent covariates such as the number of users, and the longevity of an article. This paper sets to complement the literature on the existing methodology and investigate this property in detail.

The purpose of this paper is to analyse the personalised and social characteristics of open knowledge management in higher education based on social tagging in the Web 2.0 environment.

Through the function of annotation in social tagging, the paper analyses its personalised characteristics of recognising the preferences of participants, and its personalised‐social characteristics of enriching content from all kinds of aspects; through the function of association of social tagging, it analyses its social characteristics of social networking, and its social‐personalised characteristics of collaborative acquisition or recommendation.

In the process of online information and open knowledge organisation and acquisition based on the annotation function of social tagging in the Web 2.0 environment, the personalised participation of individuals will lead to social results for everyone; however, in the process of online information and open knowledge creation and sharing based on the association function of social tagging, social and collaborative sharing among participants will help with personalised knowledge allocation.

In open knowledge management in higher education, the characteristics of personalisation and sociability based on social tagging will help to personalise the organisation and acquisition of knowledge, and help with social creation and sharing of knowledge.

This paper proposes a recommendation method that mines the semantic relationship between resources and combine it with collaborative filtering (CF) algorithm for crowdsourcing knowledge-sharing communities.

First, structured tag trees are constructed based on tag co-occurrence to overcome the tags' lack of semantic structure. Then, the semantic similarity between tags is determined based on tag co-occurrence and the tag-tree structure, and the semantic similarity between resources is calculated based on the semantic similarity of the tags. Finally, the user-resource evaluation matrix is filled based on the resource semantic similarity, and the user-based CF is used to predict the user's evaluation of the resources.

Folksonomy is a knowledge classification method that is suitable for crowdsourcing knowledge-sharing communities. The semantic similarity between resources can be obtained according to the tags in the folksonomy system, which can be used to alleviate the data sparsity and cold-start problems of CF. Experimental results show that compared with other algorithms, the algorithm in this paper performs better in mean absolute error (MAE) and F1, which indicates that the proposed algorithm yields better performance.

The proposed folksonomy-based CF method can help users in crowdsourcing knowledge-sharing communities to better find the resources they need.