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Online social networking services (SNS) have been regarded as one of the most powerful online communication channels to propagate information to other users. It means that the online social networking services are providing users with efficient features (e.g., searching, managing and visualizing new information). It is important for many online collaborative applications to understand how the information can be propagated via such social media. Thus, we want to focus on a social tagging system (e.g., Flickr) where users can easily exchange resources as well as their tags. In this paper, given a certain tag, a social pulse can be established by counting (i) the number of users and (ii) the number of resources over time. More importantly, we assume that information can be propagated by (iii) inducibility from other tags by comparing social pulses. To conduct experimentation, a tag-based searching system (called Tagoole) has been implemented to collect a dataset from Flickr. [ABSTRACT FROM AUTHOR]

We have designed and implemented an application running inside Second Life that supports user annotation of graphical objects and graphical visualization of concept ontologies, thus providing a formal, machine-accessible description of objects. As a result, we offer a platform that combines the graphical knowledge representation that is expected from a MUVE artifact with the semantic structure given by the Resource Framework Description (RDF) representation of information. [ABSTRACT FROM AUTHOR]

For our case which is Tunis, we’ve been relied on in-situ observations and results of interviews with the citizens to attempt to define; first, the status of the tree of Tunis. Then, second, we do a few assumptions about the perception of the tree by the citizens. We advance the following thesis: the social perception of the tree of Tunis is built on the personal identity of the inhabitants of the city. Nevertheless, the tree helps also to forge the identity of the urban public space by marking their life. The perception of the tree becomes a complex mix of practicality, traditions, religious beliefs and personal experiences.

In this contribution we propose a comparison between two distinct approaches to the annotation of digital resources. The former, top-down, is rooted in the cathedral model and is based on an authoritative, centralized defnition of the adopted mark-up language; the latter, bottom-up, refers to the bazaar model and is based on the contributions of a community of users. These two approaches are analyzed taking into account both their descriptive potential and the constraints they impose on the reasoning process of recommender systems, with special reference to user profling. Three case studies are described, with reference to research projects that apply these approaches in the contexts of e-learning and knowledge management.

Abstract: Smart TV, which enables viewers to conveniently access different multimedia contents and interactive services in a single platform, is currently becoming popular. The work presented here develops three new ways to enhance the performance of a smart TV. The first is to design a body control system that recognizes and interprets human gestures as machine commands to control the TV. The second is to create a new social tag-based method to recommend the most suitable multimedia contents for users. Finally, a context-aware platform is implemented to consider different environmental situations to make the best recommendations. [Copyright &y& Elsevier]

Highlights: [•] Tag frequency, recency, and duration were combined to model the personalized preference. [•] The social network was utilized to find similar users in the collaborative filtering. [•] The incorporated system was applied to the social resource sharing systems. [Copyright &y& Elsevier]

Highlights: [•] We present and compare bottom-up and top-down approaches. [•] Research topic is evaluating quality and reusability of learning objects. [•] Bottom-up approach outlines the central role of learners’ behavior. [•] Top-down methodology outlines the expert evaluation of LO quality and reusability. [•] We present examples of practical application of these approaches in eQNet project. [Copyright &y& Elsevier]

Aiming at the problem of the sparse users' access resource data resulting in low efficiency of traditional clustering algorithms, this paper proposed a tripartite iterative clustering algorithm, which cluster tags, resources and users respectively and then used the relations among them to cross iteratively adjust continuously, until all clusters achieved stability status so that the distances within the cluster were much smaller whereas the distances between the clusters were even bigger. Experiment shows that this method can effectively solve the data sparse problem, and improve the effect of user clustering. [ABSTRACT FROM AUTHOR]

The article reviews the findings and opinions of theorists and researchers who are dedicated to analyzing the phenomenon of folksonomies. We highlight the main definitions of folksonomy, their characteristics, the different types of existing folksonomies, and the differences with traditional indexing using controlled vocabularies, analyzing the advantages and disadvantages of this new model. We conclude that folksonomies can offer great advantages in information retrieval, especially if they are used to complement the indexing using controlled vocabularies. [ABSTRACT FROM AUTHOR]

With the development of the service technology, more and more organizations publish their business function as service through Internet. With the quick increase of service number, the cost of classifying these services manually is becoming more and more expensive. An automatic service classification method based on WordNet is proposed by combining text mining, semantic technique and machine learning technique. In this approach, text mining and semantic disambiguation techniques are used to obtain a set of Sense vectors with exact word meaning from description documents and social annotation of the service. These Sense vectors can describe a service. They are a set of tags of socially annotating each API. Then, a K-means algorithm is used to classify these services. At last, using the services on Programmable Web as test data, experiments demonstrate that our classification method has good effect. [ABSTRACT FROM AUTHOR]

We analyzed the context mechanism of users' decision behaviors in social tagging, and revealed the nature of social learning among users in social contexts. To improve the results of social tagging, we can select corresponding contexts according to users' given knowledge backgrounds and preferences. On the other hand, we can also recognize users' knowledge backgrounds and preferences according to their annotation results in relevant contexts. [ABSTRACT FROM AUTHOR]

In our daily lives, organizing resources like books or webpages into a set of categories to ease future access is a common task. The usual largeness of these collections requires a vast endeavor and an outrageous expense to organize manually. As an approach to effectively produce an automated classification of resources, we consider the immense amounts of annotations provided by users on social tagging systems in the form of bookmarks. In this paper, we deal with the utilization of these user-provided tags to perform a social classification of resources. For this purpose, we have created three large-scale social tagging data sets including tagging data for different types of resources, webpages and books. Those resources are accompanied by categorization data from sound expert-driven taxonomies. We analyze the characteristics of the three social tagging systems and perform an analysis on the usefulness of social tags to perform a social classification of resources that resembles the classification by experts as much as possible. We analyze six different representations using tags and compare to other data sources by using three different settings of SVM classifiers. Finally, we explore combinations of different data sources with tags using classifier committees to best classify the resources. [ABSTRACT FROM AUTHOR]

Web-based self-learning (WBSL) has received a lot of attention in recent years due to the vast amount of varied materials available in the Web 2.0 environment. However, this large amount of material also has resulted in a serious problem of cognitive overload that degrades the efficacy of learning. In this study, an information graphics method is proposed to resolve this problem. This method is based on social tagging, which is used to visualize the relationships among materials and can thus assist learners in facilitating learning. To examine the feasibility of the proposed method for managing cognitive load, an experimental model was designed in which cognitive load theory was adopted as the theoretical framework. A total of 60 university students participated in the experiment, and the partial least squares method was used to verify the experimental model. The results show that the information graphics method has a positive impact on three types of cognitive load, namely intrinsic, extraneous, and germane. Furthermore, intrinsic and germane cognitive load have a positive influence on perceived learning effectiveness, while extraneous cognitive load does not have a significant influence. One possible reason for this outcome is that the problem of visual load was not considered in the design of this study. The overall summary of the findings is that the use of social tagging can effectively manage cognitive load and positively links to perceived learning effectiveness. [ABSTRACT FROM AUTHOR]

When the Walker Art Center, in Minneapolis, Minnesota, publishes its next collection catalog online (http://collections.walkerart.org/) this year, entries on works in the collection will be extensive, dynamic, and free to anyone with an Internet connection. With high-resolution images and rich media, in-depth texts, perpetually updated information, various critical perspectives, and augmented search capabilities, the publication is expansive, alterable, and ongoing—always in production, in pace with discourses on art of our time. This new kind of catalog becomes a platform for presenting and exchanging research on works of contemporary art in a setting that allows for long-term thinking. It gives space, time, and opportunities to engage with complex concepts and processes behind works in the Walker's collection. This article briefly describes the composition of content for an online catalog entry, while discussing the evolution of contemporary arts scholarship in the era of digital humanities publishing. [ABSTRACT FROM PUBLISHER]

In the midst of the “digital revolution,” should we be pleased or disappointed about its effects in the field of art history? How does one answer this question, which is most likely premature and lacking in analytical acuity given what we know about art historical preferences and practices? But even if we accept the digital revolution as a broad concept to mean simply that image researchers have an unprecedented flood of reproductions at their fingertips, there is no question that we can still detect real feelings of unease about what this flood has brought us so far. This article takes a simple visual motif—one person carrying another—as a starting point for an iconographic exploration. In demonstrating and imagining how source material could be collected, arranged, and rearranged, it recalls in a general way the panels with photographs that Aby Warburg (1866–1929) set up to help him develop ideas and relationships in the process of his art historical research. If we could design easily accessible corpora of sources so that continuous rearrangement by visual motif and meaning were easy, and if we could seduce researchers and catalogers to cooperate in enriching them with more and better descriptions, tags, and links, the study of iconography could make a significant leap forward. [ABSTRACT FROM PUBLISHER]

The literature often views the emergence of social tagging as a potential alternative method to controlled vocabulary for organizing and indexing large-scale information resources. In this paper, we present an in-depth examination of the relationship between social tagging and controlled vocabulary-based indexing and organization in two unique contexts: the information science domain and when comparing data gathered from both English and Chinese sources. Our results show that the information science domain has more overlap between social tags and controlled vocabulary-based subject terms. This is reflected in the higher percentage of overlapping terms between tags and subject terms, as well as in the strong similarity (measured by Jaccard’s coefficient) in frequently used keywords among tags and subject terms. However, social tags in the information science domain still possess limitations in terms of uncontrolled terms, where inconsistencies and noisy usages exist. Our results also show that language difference does have an impact on social tagging. The numbers of Chinese tags overall and per book are less than those of English tags. The most frequently used English tags are single-word terms, which are different from multi-word controlled vocabulary terms. In comparison, the character difference between the most frequently used Chinese tags and Chinese subject terms is just one character (3 vs 4). However, English and Chinese users do share many similar behaviours when they tag books in the information science domain. Many of the most frequently used tags are shared between the two languages and the patterns of overlap between topical tags and subject terms are also similar between the two languages. Overall, despite the application limitations for social tagging in cataloguing and indexing, we believe that tagging has the potential to become a complementary resource for expanding and enriching controlled vocabulary systems. With the help of future technology to regulate and promote features related to controlled vocabulary in social tags, a hybrid cataloguing and indexing system that integrates social tags with controlled vocabulary would greatly improve people’s organizational and access capabilities within information resources. [ABSTRACT FROM PUBLISHER]

Users of social Web sites actively create and join communities as a way to collectively share their media content and rich experience with diverse groups of people. In this study we focus on the issue of recommending social communities (or groups) to individual users. We address specifically the potential of social tagging for accentuating users' interests and characterizing communities. We also discuss some unique methods of improving several techniques that have been adapted for use in the context of community recommendations: collaborative filtering, a random walk model, a Katz influence model, a latent semantic model, and a user-centric tag model. We effectively incorporate social tagging information in each algorithm. We present empirical evaluations using real datasets from CiteULike and Last.fm. Our experimental results demonstrate that the different algorithms incorporated with social tagging offer significant advantages in improving both the recommendation quality and coverage, and demonstrate their feasibility for community recommendations in dealing with sparsity-related limitations. [ABSTRACT FROM AUTHOR]

We propose a user model to support personalized learning paths through online material. Our approach is a variant of student modeling using the computer tutoring concept of knowledge tracing. Knowledge tracing involves representing the knowledge required to master a domain, and, from traces of online user behavior, diagnosing user knowledge states as a profile over those elements. The user model is induced from documents tagged by an expert in a social tagging system. Tags identified with 'expertise' in a domain can be used to identify a corpus of domain documents. That corpus can be fed to an automated process that distills a topic model representation characteristic of the domain. As a learner navigates and reads online material, inferences can be made about the degree to which topics in the target domain have been learned. We validate this knowledge tracing approach against data from a social tagging study. As part of this evaluation, we match the predictions of the knowledge-tracing model to individual participant responses made to individual question items used to test domain knowledge. [ABSTRACT FROM AUTHOR]

In order to adapt functionality to their individual users, systems need information about these users. The Social Web provides opportunities to gather user data from outside the system itself. Aggregated user data may be useful to address cold-start problems as well as sparse user profiles, but this depends on the nature of individual user profiles distributed on the Social Web. For example, does it make sense to re-use Flickr profiles to recommend bookmarks in Delicious? In this article, we study distributed form-based and tag-based user profiles, based on a large dataset aggregated from the Social Web. We analyze the completeness, consistency and replication of form-based profiles, which users explicitly create by filling out forms at Social Web systems such as Twitter, Facebook and LinkedIn. We also investigate tag-based profiles, which result from social tagging activities in systems such as Flickr, Delicious and StumbleUpon: to what extent do tag-based profiles overlap between different systems, what are the benefits of aggregating tag-based profiles. Based on these insights, we developed and evaluated the performance of several cross-system user modeling strategies in the context of recommender systems. The evaluation results show that the proposed methods solve the cold-start problem and improve recommendation quality significantly, even beyond the cold-start. [ABSTRACT FROM AUTHOR]

Nowadays social tagging has become a popular way to annotate, search, navigate and discover online resources, in turn leading to the sheer amount of user-generated metadata. This paper addresses the problem of recommending suitable tags during folksonomy development from a graph-based perspective. The proposed approach adapts the Katz measure, a path-ensemble based proximity measure, for the use in social tagging systems. We model a folksonomy as a weighted, undirected tripartite graph. We then apply the Katz measure to this graph, and exploit it to provide tag recommendations for individual users. We evaluate our method on two real-world folksonomies collected from CiteULike and Last.fm. The experimental results demonstrate that the proposed method improves the recommendation performance and is effective for both active taggers and cold-start taggers compared to existing algorithms. [ABSTRACT FROM AUTHOR]

Websites that provide content creation and sharing features have become quite popular recently. These sites allow users to categorize and browse content using "tags” or free-text keyword topics. Since users contribute and tag social media content across a variety of social web platforms, creating new knowledge from distributed tag data has become a matter of performing various tasks, including publishing, aggregating, integrating, and republishing tag data. In this paper, we introduce an object-centered social network based on tagging practices across different sources, and then we show how this network can be built and emerged over time. [ABSTRACT FROM PUBLISHER]

This study explores the relationships between cultural and social capital and online social tagging behaviour in Delicious.com, a social bookmarking web site that offers social tagging functionalities. Based on Bourdieu’s conception of cultural and social capital, an online questionnaire was developed to measure Delicious users’ capital possession and its influences on social tagging behavioural tendencies. The study findings showed that the offline/online cultural capital and offline social capital affected information organization-oriented tagging; offline/online social capital affected social oriented-tagging; offline/online cultural capital and offline/online social capital both affected strategic tagging; offline/online social capital affected tagging imitation. Based on the findings, we made inferences on the user roles and the power structure of a social tagging folksonomy community. [ABSTRACT FROM PUBLISHER]

The article focuses on the knowledge sharing in social network services for universities. It mentions the use of a six-tuple model for the dissemination scope of digital resources. It also notes the implementation of the use of unified labeling and multi-scale evaluation method in studying digital resources.

Abstract: Social tagging is one of the most important ways to organize and index online resources. Recommendation in social tagging systems, e.g. tag recommendation, item recommendation and user recommendation, is used to improve the quality of tags and to ease the tagging or searching process. Existing works usually provide recommendations by analyzing relation information in social tagging systems, suffering a lot from the over sparse problem. These approaches ignore information contained in the content of resources, which we believe should be considered to improve recommendation quality and to deal with the over sparse problem. In this paper we propose a recommendation approach for social tagging systems that combines content and relation analysis in a single model. By modeling the generating process of social tagging systems in a latent Dirichlet allocation approach, we build a fully generative model for social tagging, leverage it to estimate the relation between users, tags and resources and achieve tag, item and user recommendation tasks. The model is evaluated using a CiteULike data snapshot, and results show improvements in metrics for various recommendation tasks. [Copyright &y& Elsevier]

This paper explores the search effectiveness of social tagging which allows the public to freely tag resources, denoted as keywords, with any words as well as to share personal opinions on those resources. Social tagging potentially helps users to organize, manage, and retrieve resources. Efficient retrieval can help users put more of their focus on studying the resources rather than the retrieval process. This study was an investigation into the relations between social tags and user queries. Our findings were summarized into 4 main points: 1) 85% of the surveyed users agreed that social tags assisted them in searching for resources; 2) Over 40% of user queries searches, found their resources via the matching of social tags; 3) Social tags matched over 70% of user queries; 4) 14% of the social tags for a resource did not appear in the context of the resource. The experimental results demonstrated that social tags can improve users' efficiency in searching for resources. [ABSTRACT FROM AUTHOR]

This article focuses on analysing the feasibility of using social tagging as a tool for knowledge collection and retrieval in the context of the product development process (PDP). This process is a social activity that involves groups of individuals who share a common goal: 'to design a product'. Traditional knowledge-based systems (KBS) are not very well suited to capture the tacit knowledge that is embedded in this process. Social tagging is proposed in this article as the mechanism to externalize the tacit knowledge about the best CAD modelling strategies between the design team members. This knowledge is especially relevant for the management of 'engineering change orders' because this process is closely related to the modelling methodology used to create the three-dimensional (3D) CAD models that have to be adapted to accomplish a specific design modification. In order to analyse the feasibility of this approach, an experimental study was conducted to understand the tagging process in this context and the benefit of using this information in the modification procedure of 3D CAD models. Preliminary experimental results show that tagging represents a feasible approach to support knowledge collection on best CAD modelling practices. [ABSTRACT FROM AUTHOR]

Abstract: Recent years have seen an increasing diffusion of online communities giving their members the ability of specifying and sharing metadata concerning online resources. Such practice, also known as social or collaborative tagging, has the purpose of collecting and sharing opinions about Web resources and simplifying their retrieval. In this paper, we go one step further and show how tags can have more enhanced applications to be exploited for customizing Web content fruition. More precisely, we propose a multi-layer framework where data collected by social tagging communities are complemented with additional services. Such services provide users the ability of expressing their dis/agreement with existing tags, denoting the members they trust based on their characteristics and relationships, or specifying policies on which “quality” assessment of resources should be returned. Besides providing the formal specification of the proposed framework, we illustrate two case studies we have implemented and the experiments we have carried out in order to verify the feasibility of our approach. [Copyright &y& Elsevier]

Abstract: Finding and visualizing semantic relations among tags within a tag cloud enhances user experience, particularly regarding access to and retrieval of web pages on social tagging systems. Several approaches have been proposed to visualize tag relations in these systems. However, results of previous research rely on qualitative evaluation methods, and do not provide robust and sound comparison criteria. In order to allow quantitative evaluation we present a benchmark social tagging dataset, where a subset of 140 tags from a well-known social bookmarking site, delicious, have been manually categorized according to the open directory project (ODP). The manual categorization is utilized as a ground truth that enables quantitative evaluation providing a way of inferring the best of different clustering approaches. With this dataset we also explore different tag representation approaches to present a reorganized tag cloud by using self organizing maps. In addition, we present an approach to enrich the resultant tag cloud with the most characteristic terms for each tag and group of tags, making possible a further filtered navigation, both by tag and document content, and easing a deeper qualitative evaluation of the clusters. [Copyright &y& Elsevier]

Abstract: For the last 15 years, there have been various research and great advances in the field of content-based search and retrieval of images. In this paper, we present an overview of the JPSearch Standard, which is a recently published set of international standards providing interoperable framework for image search and sharing. The JPSearch standard is composed of six parts, which are Part 1: System framework and components, Part 2: Registration, identification and management of schema and ontology, Part 3: Query format, Part 4: File format for metadata embedded in image data (JPEG and JPEG 2000), Part 5: Data interchange format between image repositories, Part 6: Reference software. The Part 1 provides motivation and overview of the JPSearch framework. The Part 2 supports interoperability among various metadata specifications and social tagging using the specification of core metadata and translation rule description language. The Part 3 provides powerful image query language for interoperability among multiple image databases. The Part 4 specifies a file format based on JPEG and JPEG 2000 file format in which arbitrary number of metadata description of a image can be embedded. The Part 5 supports interoperability in interchanging images with metadata between image repositories. Finally, the Part 6 provides reference and utility softwares based on which the JPSearch compliant systems can be easily developed. In this paper, summaries and usage examples for each part of the JPSearch standard are presented. [Copyright &y& Elsevier]

Keywords have played an important role not only for searchers who formulate a query, but also for search engines that index documents and evaluate the query. Recently, tags chosen by users to annotate web resources are gaining significance for improving information retrieval (IR) tasks, in that they can act as meaningful keywords bridging the gap between humans and machines. One critical aspect of tagging (besides the tag and the resource) is the user (or tagger); there exists a ternary relationship among the tag, resource, and user. The traditional inverted index, however, does not consider the user aspect, and is based on the binary relationship between term and document. In this paper we propose a social inverted index – a novel inverted index extended for social-tagging-based IR – that maintains a separate user sublist for each resource in a resource-posting list to contain each user’s various features as weights. The social inverted index is different from the normal inverted index in that it regards each user as a unique person, rather than simply count the number of users, and highlights the value of a user who has participated in tagging. This extended structure facilitates the use of dynamic resource weights, which are expected to be more meaningful than simple user-frequency-based weights. It also allows a flexible response to the conditional queries that are increasingly required in tag-based IR. Our experiments have shown that this user-considering indexing performs better in IR tasks than a normal inverted index with no user sublists. The time and space overhead required for index construction and maintenance was also acceptable. [ABSTRACT FROM PUBLISHER]

We describe a web-based educational platform relying on the use of tags as learning resources in a social context. Tags are user-generated keywords used to organise and improve access to online information resources. Social tagging refers to the practice of labelling or categorising resources in a shared online environment. Our platform, named tag-based collaborative system (TaCS), is a first attempt at implementing a recent theoretical framework specific to social tagging that defines learning and knowledge building as a co-evolution of cognitive and social systems. Within this framework, design choices have been made to support individual, social and collaborative learning processes. To illustrate the usability of TaCS, we describe an example of its integration in a real course during five weeks. Observed uses and opinions expressed by students tend to validate the implementation of the theoretical framework, and the value of TaCS as an educational platform. [ABSTRACT FROM AUTHOR]

Tag recommendation encourages users to add more tags in bridging the semantic gap between human concept and the features of media object, which provides a feasible solution for content-based multimedia information retrieval. In this paper, we study personalized tag recommendation in a popular online photo sharing site - Flickr. Social relationship information of users is collected to generate an online social network. From the perspective of network topology, we propose node topological potential to characterize user's social influence. With this metric, we distinguish different social relations between users and find out those who really have influence on the target users. Tag recommendations are based on tagging history and the latent personalized preference learned from those who have most influence in user's social network. We evaluate our method on large scale real-world data. The experimental results demonstrate that our method can outperform the non-personalized global co-occurrence method and other two state-of-the-art personalized approaches using social networks. We also analyze the further usage of our approach for the cold-start problem of tag recommendation. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, social Web users have been overwhelmed by the huge numbers of social media available. Consequentially, users have trouble finding social media suited to their needs. To help such users retrieve useful social media content, we propose a new model of tag-based personalized searches to enhance not only retrieval accuracy but also retrieval coverage. By leveraging social tagging as a preference indicator, we build two models: (i) a latent tag preference model that reflects how a certain user has assigned tags similar to a given tag and (ii) a latent tag annotation model that captures how users have tagged a certain tag to resources similar to a given resource. We then seamlessly map the tags onto items, depending on an individual user''s query, to find the most desirable content relevant to the user''s needs. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the art algorithms and show our method''s feasibility for personalized searches in social media services. [Copyright &y& Elsevier]

Abstract: The World Wide Web provides a tremendously large quantity of information. When users search for information or products on the Web, they will presumably be inclined to choose their path of navigation on the basis of their prior knowledge. In those cases in which the prior knowledge of users is incorrect, however, this navigation process is assumed to lead to suboptimal search results. In an experimental study with 180 participants, we examined to what extent both the users’ prior knowledge and social tags – which capture the collective knowledge of a Web community in tag clouds – influenced the navigation of users and triggered incidental learning processes during the Web search. The results supported the assumption that the users’ prior knowledge is indeed crucial for navigation, as users followed those tags which corresponded to their internal associations. Moreover, we found that social tags also affected the navigation behavior of users, as a strong collective association of social tags led to a high selection rate for these tags. Finally, the results showed that social tags triggered incidental learning processes, as those internal associations which corresponded to tags with a strong collective association were strengthened during navigation. The implications of these findings for further research are discussed. [Copyright &y& Elsevier]

Background: Over the past years, the number of available informatics resources in medicine has grown exponentially. While specific inventories of such resources have already begun to be developed for Bioinformatics (BI), comparable inventories are as yet not available for the Medical Informatics (MI) field, so that locating and accessing them currently remains a difficult and time-consuming task. Description: We have created a repository of MI resources from the scientific literature, providing free access to its contents through a web-based service. We define informatics resources as all those elements that constitute, serve to define or are used by informatics systems, ranging from architectures or development methodologies to terminologies, vocabularies, databases or tools. Relevant information describing the resources is automatically extracted from manuscripts published in top-ranked MI journals. We used a pattern matching approach to detect the resources' names and their main features. Detected resources are classified according to three different criteria: functionality, resource type and domain. To facilitate these tasks, we have built three different classification schemas by following a novel approach based on folksonomies and social tagging. We adopted the terminology most frequently used by MI researchers in their publications to create the concepts and hierarchical relationships belonging to the classification schemas. The classification algorithm identifies the categories associated with resources and annotates them accordingly. The database is then populated with this data after manual curation and validation. Conclusions: We have created an online repository of MI resources to assist researchers in locating and accessing the most suitable resources to perform specific tasks. The database contains 609 resources at the time of writing and is available at http://www.gib.fi.upm.es/eMIR2. We are continuing to expand the number of available resources by taking into account further publications as well as suggestions from users and resource developers. [ABSTRACT FROM AUTHOR]

We propose a semantic collaborative filtering method to enhance recommendation quality derived from user-generated tags. Social tagging is employed as an approach in order to grasp and filter users' preferences for items. In addition, we explore several advantages of semantic tagging for ambiguity, synonymy, and semantic interoperability, which are notable challenges in information filtering. The proposed approach first determines semantically similar users using social tagging and subsequently discovers semantically relevant items for each user. Experimental results show that our method offers significant advantages both in terms of improving the recommendation quality and in dealing with ambiguity, synonymy, and interoperability issues. [ABSTRACT FROM AUTHOR]

The huge amount of user-generated content produced in Web 2.0 needs structures if this content should be used in a meaningful way. Folksonomies are a breed of the Web for indexing digital resources with free user-generated tags, which is both beneficial and risky at the same time. This paper deals with the basic concepts of folksonomies and discusses their pros and cons for knowledge representation purposes. Moreover, it is shown how libraries can use folksonomies in their daily work. [ABSTRACT FROM AUTHOR]

Abstract: Social tagging is widely practiced in the Web 2.0 era. Users can annotate useful or interesting Web resources with keywords for future reference. Social tagging also facilitates sharing of Web resources. This study reviews the chronological variation of social tagging data and tracks social trends by clustering tag time series. The data corpus in this study is collected from Hemidemi.com. A tag is represented in a time series form according to its annotating Web pages. Then time series clustering is applied to group tag time series with similar patterns and trends in the same time period. Finally, the similarities between clusters in different time periods are calculated to determine which clusters have similar themes, and the trend variation of a specific tag in different time periods is also analyzed. The evaluation shows the recommendation accuracy of the proposed approach is about 75%. Besides, the case discussion also proves the proposed approach can track the social trends. [Copyright &y& Elsevier]

Tags are widely used in Web 2.0 applications, such as blogs and online bookmarks. Many characteristics of tags have been studied in previous works, but the granularity level of tags still remains untouched. Granularity level describes how focused a tag is, the lower the level, the more concrete concept the tag covers. The paper has three main contributions: 1) We propose a method to discover the concept level of tags automatically. 2) We apply the method to a real-world tag data set, and study the distribution of levels. 3) We perform tag recommendation concerning their concept levels. Experiments are conducted on a large-scale real world Chinese blog data set. We find that most tags are in more concrete levels, and the users tend to choose tags within the same concept level for a document. When predicting tags for a new document, tags in higher concept levels provide little help, yet may hurt the performance. [ABSTRACT FROM AUTHOR]

Social tagging is a major characteristic of Web 2.0. A social tagging system can be modeled with a tripartite network of users, resources, and tags. In this paper, we investigate how to enhance Web clustering by leveraging the tripartite network of social tagging systems. We propose a clustering method called “Tripartite Clustering” which clusters the three types of nodes (resources, users, and tags) simultaneously by only utilizing the links in the social tagging network. We also investigate two other approaches to exploit social tagging for clustering with K-means and Link K-means. All the clustering methods are experimented on a real-world social tagging data set sampled from del.icio.us. The clustering results are evaluated against a human-maintained Web directory. The experimental results show that the social tagging network is a very useful information source for document clustering. All social-annotation-based clustering methods can significantly improve the performance of content-based clustering. Compared to social-annotation-based K-means and Link K-means, Tripartite Clustering achieves equivalent or better performance and produces more useful information. [ABSTRACT FROM PUBLISHER]

In social tagging systems such as Delicious and Flickr, users collaboratively manage tags to annotate resources. Naturally, a social tagging system can be modeled as a (user, tag, resource) hypernetwork, where there are three different types of nodes, namely users, resources and tags, and each hyperedge has three end nodes, connecting a user, a resource and a tag that the user employs to annotate the resource. Then how can we automatically cluster related users, resources and tags, respectively? This is a problem of community detection in a 3-partite, 3-uniform hypernetwork. More generally, given a K-partite K-uniform (hyper)network, where each (hyper)edge is a K-tuple composed of nodes of K different types, how can we automatically detect communities for nodes of different types? In this paper, by turning this problem into a problem of finding an efficient compression of the (hyper)network's structure, we propose a quality function for measuring the goodness of partitions of a K-partite K-uniform (hyper)network into communities, and develop a fast community detection method based on optimization. Our method overcomes the limitations of state of the art techniques and has several desired properties such as comprehensive, parameter-free, and scalable. We compare our method with existing methods in both synthetic and real-world datasets. [ABSTRACT FROM AUTHOR]