Search (187 results, page 1 of 10)

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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/.

Theme

Social tagging

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Date

22. 3.2015 9:30:24

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Abstract

This paper describes a survey on how system vendors and libraries handled tagging in OPACs and discovery layers. Tags are user added subject metadata, also called folksonomies. This survey also investigated user behavior when they face the possibility to tag. The findings indicate that legacy/classic systems have no tagging capability. About 47% of the discovery tools provide tagging function. About 49% of the libraries that have a system with tagging capability have turned the tagging function on in their OPACs and discovery tools. Only 40% of the libraries that turned tagging on actually utilized user added subject metadata as access point to collections. Academic library users are less active in tagging than public library users.

Theme

Social tagging

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Abstract

Neue Webdienste vereinen die Vorteile von Thesauri und durch privates "Tagging" gewonnenen "Ego-Daten".

Theme

Social tagging

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Abstract

Although user tagging of library resources shows substantial promise as a means of improving the quality of users' access to those resources, several important questions about the level and nature of the warrant for basing retrieval tools on user tagging are yet to receive full consideration by library practitioners and researchers. Among these is the simple evaluative question: What, specifically, are the factors that determine whether or not user-tagging services will be successful? If success is to be defined in terms of the effectiveness with which systems perform the particular functions expected of them (rather than simply in terms of popularity), an understanding is needed both of the multifunctional nature of tagging tools, and of the complex nature of users' mental models of that multifunctionality. In this paper, a conceptual framework is developed for the evaluation of systems that integrate user tagging with more traditional methods of library resource description.

Theme

Social tagging

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Abstract

This paper explores the system of categories that is used to classify articles in Wikipedia. It is compared to collaborative tagging systems like del.icio.us and to hierarchical classification like the Dewey Decimal Classification (DDC). Specifics and commonalities of these systems of subject indexing are exposed. Analysis of structural and statistical properties (descriptors per record, records per descriptor, descriptor levels) shows that the category system of Wikimedia is a thesaurus that combines collaborative tagging and hierarchical subject indexing in a special way.

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Abstract

The aim of the present study was to identify and categorize social tagging trends and developments as revealed by the analysis of library and information science scholarly and professional literature.

Theme

Social tagging

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Abstract

Recently, a growing amount of systems that allow personal content annotation (tagging) are being created, ranging from personal sites for organising bookmarks (del.icio.us), photos (flickr.com) or videos (video.google.com, youtube.com) to systems for managing bibliographies for scientific research projects (citeulike.org, connotea.org). Simultaneously, a debate on the pro and cons of allowing users to add personal keywords to digital content has arisen. One recurrent point-of-discussion is whether tagging can solve the well-known vocabulary problem: In order to support successful retrieval in complex environments, it is necessary to index an object with a variety of aliases (cf. Furnas 1987). In this spirit, social tagging enhances the pool of rigid, traditional keywording by adding user-created retrieval vocabularies. Furthermore, tagging goes beyond simple personal content-based keywords by providing meta-keywords like funny or interesting that "identify qualities or characteristics" (Golder and Huberman 2006, Kipp and Campbell 2006, Kipp 2007, Feinberg 2006, Kroski 2005). Contrarily, tagging systems are claimed to lead to semantic difficulties that may hinder the precision and recall of tagging systems (e.g. the polysemy problem, cf. Marlow 2006, Lakoff 2005, Golder and Huberman 2006). Empirical research on social tagging is still rare and mostly from a computer linguistics or librarian point-of-view (Voß 2007) which focus either on the automatic statistical analyses of large data sets, or intellectually inspect single cases of tag usage: Some scientists studied the evolution of tag vocabularies and tag distribution in specific systems (Golder and Huberman 2006, Hammond 2005). Others concentrate on tagging behaviour and tagger characteristics in collaborative systems. (Hammond 2005, Kipp and Campbell 2007, Feinberg 2006, Sen 2006). However, little research has been conducted on the functional and linguistic characteristics of tags.1 An analysis of these patterns could show differences between user wording and conventional keywording. In order to provide a reasonable basis for comparison, a classification system for existing tags is needed.
Therefore our main research questions are as follows: - Is it possible to discover regular patterns in tag usage and to establish a stable category model? - Does a specific tagging language comparable to internet slang or chatspeak evolve? - How do social tags differ from traditional (author / expert) keywords? - To what degree are social tags taken from or findable in the full text of the tagged resource? - Do tags in a research literature context go beyond simple content description (e.g. tags indicating time or task-related information, cf. Kipp et al. 2006)?

Theme

Social tagging

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Source

http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CDQQFjAE&url=http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F3131107&ei=HzFWVYvGMsiNsgGTyoFI&usg=AFQjCNE2FHUeR9oQTQlNC4TPedv4Mo3DaQ&sig2=Rlzpr7a3BLZZkqZCXXN_IA&bvm=bv.93564037,d.bGg&cad=rja

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Content

Vortrag anlässlich des Workshops: "Extending the multilingual capacity of The European Library in the EDL project Stockholm, Swedish National Library, 22-23 November 2007".

Theme

Social tagging

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Source

https%3A%2F%2Ftannerlectures.utah.edu%2F_documents%2Fa-to-z%2Fp%2Fpopper80.pdf&usg=AOvVaw3f4QRTEH-OEBmoYr2J_c7H

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Abstract

The object of Information Retrieval is to retrieve all relevant documents for a user query and only those relevant documents. Much research has focused on achieving this objective with little regard for storage overhead or performance. In the paper we evaluate the use of Part of Speech Tagging to improve, the index storage overhead and general speed of the system with only a minimal reduction to precision recall measurements. We tagged 500Mbs of the Los Angeles Times 1990 and 1989 document collection provided by TREC for parts of speech. We then experimented to find the most relevant part of speech to index. We show that 90% of precision recall is achieved with 40% of the document collections terms. We also show that this is a improvement in overhead with only a 1% reduction in precision recall.

Object

POS-Tagging

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Theme

Social tagging

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Theme

Social tagging

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Abstract

KoT (Kinds of Tags) is an ongoing joint collaborative research effort with many participants worldwide, including the University of Minho, UKOLN, the University of Bologna, the Université Libre de Bruxelles and La Universidad Carlos III de Madrid. It is focused on the analysis of tags that are in common use in the practice of social tagging, with the aim of discovering how easily tags can be 'normalised' for interoperability with standard metadata environments such as the DC Metadata Terms.

Theme

Social tagging

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Abstract

Today, in times of Web 2.0., graduates and undergraduates interact in virtual communities like studiVZ (Studentenverzeichnis) and generate content by reviewing or tagging documents. This phenomenon offers good prospects for academic libraries. They can use the customers' tags for indexing the growing amount of electronic resources and thereby optimize the search for these documents. Important examples are the journals, databases and e-books included in the "Nationallizenzen" financed by the German Research Foundation (DFG). The documents in this collection are not manually indexed by librarians and have no annotation according to the German standard classification systems. Connecting search systems by means of Web-2.0.-services is an important task for libraries. For this purpose users are encouraged to tag printed and electronic resources in search systems like the libraries' online catalogs and to establish connections between entries in other systems, e.g. Bibsonomy, and the items found in the online catalog. As a consequence annotations chosen by both, users and librarians, will coexist: The items in the tagging systems and the online catalog are linked, library users may find other publications of interest, and contacts between library users with similar scientific interests may be established. Librarians have to face the fact that user generated tags do not necessarily have the same quality as their own annotations and will therefore have to seek for instruments for comparing user generated tags with library generated keywords.

Theme

Social tagging

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Abstract

Autonomy's Classification solutions remove the necessity for organizations to rely on human intervention or manual processing of information, such as manual tagging, typically required to make most other e-business applications work. Autonomy's ability to consistently and accurately classify data automatically is a unique infrastructure solution that overcomes the predicaments surrounding the exponential growth of unstructured data.

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