Web 2.0 and folksonomies in a library context

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2011 ElsevierLibraries have a societal purpose and this role has become increasingly important as new technologies enable organizations to support, enable and enhance the participation of users in assuming an active role in the creation and communication of information. Folksonomies, a Web 2.0 technology, represent such an example. Folksonomies result from individuals freely tagging resources available to them on a computer network. In a library environment folksonomies have the potential of overcoming certain limitations of traditional classification systems such as the Library of Congress Subject Headings (LCSH). Typical limitations of this type of classification systems include, for example, the rigidity of the underlying taxonomical structures and the difficulty of introducing change in the categories. Folksonomies represent a supporting technology to existing classification systems helping to describe library resources more flexibly, dynamically and openly. As a review of the current literature shows, the adoption of folksonomies in libraries is novel and limited research has been carried out in the area. This paper presents research into the adoption of folksonomies for a University library. A Web 2.0 system was developed, based on the requirements collected from library stakeholders, and integrated with the existing library computer system. An evaluation of the work was carried out in the form of a survey in order to understand the possible reactions of users to folksonomies as well as the effects on their behavior. The broad conclusion of this work is that folksonomies seem to have a beneficial effect on users’ involvement as active library participants as well as encourage users to browse the catalogue in more depth

Digital learning objects: a local response to the California State University system initiative

The purpose of this paper is to present a virtual library plan created by library directors of the 23 California State University (CSU) system campuses. The information literacy portion of the project offers a repository of high quality interactive digital learning objects (DLOs) in the MERLOT repository. Therefore, DLOs created locally at the Dr Martin Luther King, Jr Library at San José State University (SJSU) focus on topics that supplement the “core” DLO collection

Enriching ontological user profiles with tagging history for multi-domain recommendations

Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites

Ensuring the discoverability of digital images for social work education : an online tagging survey to test controlled vocabularies

The digital age has transformed access to all kinds of educational content not only in text-based format but also digital images and other media. As learning technologists and librarians begin to organise these new media into digital collections for educational purposes, older problems associated with cataloguing and classifying non-text media have re-emerged. At the heart of this issue is the problem of describing complex and highly subjective images in a reliable and consistent manner. This paper reports on the findings of research designed to test the suitability of two controlled vocabularies to index and thereby improve the discoverability of images stored in the Learning Exchange, a repository for social work education and research. An online survey asked respondents to "tag", a series of images and responses were mapped against the two controlled vocabularies. Findings showed that a large proportion of user generated tags could be mapped to the controlled vocabulary terms (or their equivalents). The implications of these findings for indexing and discovering content are discussed in the context of a wider review of the literature on "folksonomies" (or user tagging) versus taxonomies and controlled vocabularies

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

FolkRank: A Ranking Algorithm for Folksonomies

In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. A long version of this paper has been published at the European Semantic Web Conference 2006

Challenges in Bridging Social Semantics and Formal Semantics on the Web

This paper describes several results of Wimmics, a research lab which names stands for: web-instrumented man-machine interactions, communities, and semantics. The approaches introduced here rely on graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The re-search results are applied to support and foster interactions in online communities and manage their resources

Evaluating the semantic web: a task-based approach

The increased availability of online knowledge has led to the design of several algorithms that solve a variety of tasks by harvesting the Semantic Web, i.e. by dynamically selecting and exploring a multitude of online ontologies. Our hypothesis is that the performance of such novel algorithms implicity provides an insight into the quality of the used ontologies and thus opens the way to a task-based evaluation of the Semantic Web. We have investigated this hypothesis by studying the lessons learnt about online ontologies when used to solve three tasks: ontology matching, folksonomy enrichment, and word sense disambiguation. Our analysis leads to a suit of conclusions about the status of the Semantic Web, which highlight a number of strengths and weaknesses of the semantic information available online and complement the findings of other analysis of the Semantic Web landscape