Search (8 results, page 1 of 1)

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Abstract

Folksonomy is the result of describing Web resources with tags created by Web users. Although it has become a popular application for the description of resources, in general terms Folksonomies are not being conveniently integrated in metadata. However, if the appropriate metadata elements are identified, then further work may be conducted to automatically assign tags to these elements (RDF properties) and use them in Semantic Web applications. This article presents research carried out to continue the project Kinds of Tags, which intends to identify elements required for metadata originating from folksonomies and to propose an application profile for DC Social Tagging. The work provides information that may be used by software applications to assign tags to metadata elements and, therefore, means for tags to be conveniently gathered by metadata interoperability tools. Despite the unquestionably high value of DC and the significance of the already existing properties in DC Terms, the pilot study show revealed a significant number of tags for which no corresponding properties yet existed. A need for new properties, such as Action, Depth, Rate, and Utility was determined. Those potential new properties will have to be validated in a later stage by the DC Social Tagging Community.

Pages

S.14-22

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

Type

a

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Abstract

There is a growing interest into how we represent and share tagging data in collaborative tagging systems. Conventional tags, meaning freely created tags that are not associated with a structured ontology, are not naturally suited for collaborative processes, due to linguistic and grammatical variations, as well as human typing errors. Additionally, tags reflect personal views of the world by individual users, and are not normalised for synonymy, morphology or any other mapping. Our view is that the conventional approach provides very limited semantic value for collaboration. Moreover, in cases where there is some semantic value, automatically sharing semantics via computer manipulations is extremely problematic. This paper explores these problems by discussing approaches for collaborative tagging activities at a semantic level, and presenting conceptual models for collaborative tagging activities and folksonomies. We present criteria for the comparison of existing tag ontologies and discuss their strengths and weaknesses in relation to these criteria.

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

Type

a

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Abstract

With the increasing popularity of social tagging systems, the potential for using social tags as a source of metadata is being explored. Social tagging systems can simplify the involvement of a large number of users and improve the metadata-generation process. Current research is exploring social tagging systems as a mechanism to allow nonprofessional catalogers to participate in metadata generation. Because social tags are not from controlled vocabularies, there are issues that have to be addressed in finding quality terms to represent the content of a resource. This research explores ways to obtain a set of tags representing the resource from the tags provided by users. Two metrics are introduced. Annotation Dominance (AD) is a measure of the extent to which a tag term is agreed to by users. Cross Resources Annotation Discrimination (CRAD) is a measure of a tag's potential to classify a collection. It is designed to remove tags that are used too broadly or narrowly. Using the proposed measurements, the research selects important tags (meta-terms) and removes meaningless ones (tag noise) from the tags provided by users. To evaluate the proposed approach to find classificatory metadata candidates, we rely on expert users' relevance judgments comparing suggested tag terms and expert metadata terms. The results suggest that processing of user tags using the two measurements successfully identifies the terms that represent the topic categories of web resource content. The suggested tag terms can be further examined in various usages as semantic metadata for the resources.

Type

a

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Abstract

Social tagging, hashtags, and geotags are used across a variety of platforms (Twitter, Facebook, Tumblr, WordPress, Instagram) in different countries and cultures. This book, representing researchers and practitioners across different information professions, explores how social tags can link content across a variety of environments. Most studies of social tagging have tended to focus on applications like library catalogs, blogs, and social bookmarking sites. This book, in setting out a theoretical background and the use of a series of case studies, explores the role of hashtags as a form of linked data?without the complex implementation of RDF and other Semantic Web technologies.

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Source

Social Semantic Web: Web 2.0, was nun? Hrsg.: A. Blumauer u. T. Pellegrini

Type

a

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Abstract

The Digital Commons platform for institutional repositories provides a three-tiered taxonomy of academic disciplines for each item submitted to the repository. Since faculty and departmental administrators across campuses are encouraged to submit materials to the institutional repository themselves, they must also assign disciplines or subject categories for their own work. The expandable drop-down menu of about 1,000 categories is easy to use, and facilitates the growth of the institutional repository and access to the materials through the Internet.

Footnote

Contribution in a special issue "Beyond libraries: Subject metadata in the digital environment and Semantic Web" - Enthält Beiträge der gleichnamigen IFLA Satellite Post-Conference, 17-18 August 2012, Tallinn.

Type

a

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Abstract

Purpose: Investigate the combination of controlled and folksonomy approaches to support resource discovery in repositories and digital collections. Aim: Investigate whether use of an established controlled vocabulary can help improve social tagging for better resource discovery. Objectives: (1) Investigate indexing aspects when using only social tagging versus when using social tagging with suggestions from a controlled vocabulary; (2) Investigate above in two different contexts: tagging by readers and tagging by authors; (3) Investigate influence of only social tagging versus social tagging with a controlled vocabulary on retrieval. - Vgl.: http://www.ukoln.ac.uk/projects/enhanced-tagging/.