Researchers are daily engaged in bibliographic tasks concerning literature search and review, both in the role of authors of scientific papers and when they are reviewers or evaluators. Current indexing platforms poorly support the visual exploration and comparative metadata analysis coming from subsequent searches. To address these issues, we designed and realized VisualBib(va), an online visual analytics solution, where a visual environment includes analysis control, bibliography exploration, automatic metadata extraction, and metrics visualization for real-time scenarios. We introduce and discuss here the relevant functions that VisualBib(va) supports through two usage scenarios related to the creation and the review of a bibliography. Full details about the VisualBib(va) design, implementation and evaluation are available in [3]. A fully interactive environment is available at http://visualbib.uniud.it/ (video demo: http://bit.ly/3fKuZNg).

Abstract The amount of scientific papers is growing together with the development of science itself; but, although there is an unprecedented availability of large citation indexes, some daily activities of researchers remain time‐consuming and poorly supported. In this paper, we present Visual Bibliographies (VisualBib), a real‐time visual platform, designed using a zz‐structure‐based model for linking metadata and a narrative, visual approach for showing bibliographies. VisualBib represents a usable, advanced, and visual tool, which simplifies the management of bibliographies, supports a core set of bibliographic tasks, and helps researchers during complex analyses on scientific bibliographies. We present the variety of metadata formats and visualization methods, proposing two use case scenarios. The maturity of the system implementation allowed us two studies, for evaluating both the effectiveness of VisualBib in providing answers to specific data analysis tasks and to support experienced users during real‐life uses. The results of the evaluation are positive and describe an effective and usable platform.

The availability of hundreds of Web 2.0 and mobile applications represent a great opportunity in helping teachers adopt student centred methodologies. These applications support users in creating and sharing digital artefacts, in aggregating, remixing and collecting heterogeneous materials and in communicating within working groups. When incorporated into daily teaching and learning activities (TLA), such applications can improve the collaborative, cognitive and creative work of the students, enhancing and redefining traditional educational practices. Nevertheless, although these applications are generally easy to find and use, there is a lack of knowledge about their existence, their functions and their potential in an educational setting. In this paper we describe AppInventory, a novel platform which contains a digital catalogue of applications: they are organized by applying an original taxonomy; the users can browse among them benefiting from a visual approach and semantic connections modelled using zz-structures.

Existing citation indexes of scientific literature, like Scopus, WOS, Google Scholar and OpenAire, collect metadata of papers and authors. By using appropriate queries, it is possible to access these metadata, find specific papers and all associated details, as well as retrieve indices, metrics and information about authors. However, a big issue is that they generally do not aggregate the results of subsequent searches and do not offer explicit representations of author/citation relationships between found items. This paper introduces VisualBib, a Web application prototype conceived to support researchers who wish to create, modify, visualize and share bibliographies. Starting with a small set of papers or with a restricted number of authors, it generates, in real-time, an interactive visual representation of the corresponding bibliography; the user can explore the network of cited/citing references and dynamically add new papers in order to build up customized bibliographies which are represented using holistic, aggregated and graphical views.

SUMMARY Tag‐based systems are widely available, thanks to their intrinsic advantages, such as self‐organization, currency, and ease of use. Although they represent a precious source of semantic metadata, their utility is still limited. The inherent lexical ambiguities of tags strongly affect the extraction of structured knowledge and the quality of tag‐based recommendation systems. In this paper, we propose a methodology for the analysis of tag‐based systems, addressing tag synonymy and homonymy at the same time in a holistic approach: in more detail, we exploit a tripartite graph to reduce the problem of synonyms and homonyms; we apply a customized version of Tag Context Similarity to detect them, overcoming the limitations of current similarity metrics; finally, we propose the application of an overlapping clustering algorithm to detect contexts and homonymies, then evaluate its performances, and introduce a methodology for the interpretation of its results. Copyright © 2012 John Wiley & Sons, Ltd.

The Social Web is now a successful reality with its quickly growing number of users and applications. Also the Semantic Web, which started with the objective of describing Web resources in a machine-processable way, is now outgrowing the research labs and is being massively exploited in many websites, incorporating high-quality user-generated content and semantic annotations. The primary goal of this special section is to showcase some recent research at the intersection of the Social Web and the Semantic Web that explores the benefits that adaptation and personalization have to offer in the Web of the future, the so-called Social Adaptive Semantic Web. We have selected two articles out of fourteen submissions based on the quality of the articles and we present the main lessons learned from the overall analysis of these submissions.

Collaborative tagging represents for the Web a potential way for organizing and sharing information and for heightening the capabilities of existing search engines. However, because of the lack of automatic methodologies for generating the tags and supporting the tagging activity, many resources on the Web are deficient in tag information, and recommending opportune tags is both a current open issue and an exciting challenge. This paper approaches the problem by applying a combined set of techniques and tools (that uses tags, domain ontologies, keyphrase extraction methods) thereby generating tags automatically. The proposed approach is implemented in the PIRATES (Personalized Intelligent tag Recommender and Annotator TEStbed) framework, a prototype system for personalized content retrieval, annotation, and classification. A case study application is developed using a domain ontology for software engineering. © 2010 Wiley Periodicals, Inc.

Web 2.0 applications innovate traditional informative services providing Web users with a set of tools for publishing and sharing information. Social bookmarking systems are an interesting example of this trend where users generate new contents. Unfortunately, the growing amount of available resources makes hard the task of accessing to relevant information in these environments. Recommender systems face this problem filtering relevant resources connected to users' interests and preferences. In particular, collaborative filtering recommender systems produce suggestions using the opinions of similar users, called the neighbors. The task of finding neighbors is difficult in environment such as social bookmarking systems, since bookmarked resources belong to different domains. In this paper we propose a methodology for partitioning users, tags and resources into domains of interest. Filtering tags and resources in accordance to the specific domains we can select a different set of neighbors for each domain, improving the accuracy of recommendations.

We present an open, concurrent model of adaptive hypermedia; all its functionality and data are completely distributed on a web of autonomous actors. The model that enables the system to interact adaptively is a fuzzy stereotype-based user model. Its presentation and application are exploited in the cooperative actor-based environment, generated from the hypermedia model. The merge of these two paradigms offers a new concurrent and fuzzy approach in the adaptive hypermedia system domain.

The market for parallel and distributed computing systems keeps growing. Technological advances in processor power, networking, telecommunication and multimedia are stimulating the development of applications requiring parallel and distributed computing. An important research problem in this area is the need to find a robust bridge between the decentralisation of knowledge sources in information-based systems and the distribution of computational power. Consequently, the attention of the research community has been directed towards high-level, concurrent, distributed programming. This work proposes a new hypermedia framework based on the metaphor of the actor model. The storage and run-time layers are represented entirely as communities of independent actors that cooperate in order to accomplish common goals, such as version management or user adaptivity. These goals involve fundamental and complex hypermedia issues, which, thanks to the distribution of tasks, are treated in an efficient and simple way.

In this paper it is shown how structural and cognitive versioning issues can be efficiently managed in a Petri nets based hypertextual model. The advantages of this formalism are enhanced by modular and structured modeling; modularity allows to focus the attention only on some modules, while giving the abstraction of the others. Each module owns metaknowledge that is useful in defining new layers and contexts.