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Footnote

Part of a section: "Papers from the Fourth North American Symposium on Knowledge Organization, sponsored by ISKO-Canada, United States, 13-14 June, 2013, Milwaukee, Wisconsin"

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Abstract

Large image collections on the Web need to be organized for effective retrieval. Metadata has a key role in image retrieval but rely on professionally assigned tags which is not a viable option. Current content-based image retrieval systems have not demonstrated sufficient utility on large-scale image sources on the web, and are usually used as a supplement to existing text-based image retrieval systems. We present two social tagging alternatives in the form of photo-sharing networks and image labeling games. Here we analyze these applications to evaluate their usefulness from the semantic point of view, investigating the management of social tagging for indexing. The findings of the study have shown that social tagging can generate a sizeable number of tags that can be classified as in terpretive for an image, and that tagging behaviour has a manageable and adjustable nature depending on tagging guidelines.

Content

Papers from the ISKO-UK Biennial Conference, "Knowledge Organization: Pushing the Boundaries," United Kingdom, 8-9 July, 2013, London.

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Imprint

Medford, N.J. : Information Today

LCSH

Libraries / Information technology
Online social networks

Subject

Libraries / Information technology
Online social networks

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Abstract

The presence of social networks in complex systems has made networks and community structure a focal point of study in many domains. Previous studies have focused on the structural emergence and growth of communities and on the topics displayed within the network. However, few scholars have closely examined the relationship between the thematic and structural properties of networks. Therefore, this article proposes the Tagger Tag Resource-Latent Dirichlet Allocation-Community model (TTR-LDA-Community model), which combines the Latent Dirichlet Allocation (LDA) model with the Girvan-Newman community detection algorithm through an inference mechanism. Using social tagging data from Delicious, this article demonstrates the clustering of active taggers into communities, the topic distributions within communities, and the ranking of taggers, tags, and resources within these communities. The data analysis evaluates patterns in community structure and topical affiliations diachronically. The article evaluates the effectiveness of community detection and the inference mechanism embedded in the model and finds that the TTR-LDA-Community model outperforms other traditional models in tag prediction. This has implications for scholars in domains interested in community detection, profiling, and recommender systems.

Source

Journal of the American Society for Information Science and Technology. 62(2011) no.9, S.1849-1866

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Abstract

Purpose - Traditional subject indexing and classification are considered infeasible in many digital collections. This paper seeks to investigate ways of enhancing social tagging via knowledge organization systems, with a view to improving the quality of tags for increased information discovery and retrieval performance. Design/methodology/approach - Enhanced tagging interfaces were developed for exemplar online repositories, and trials were undertaken with author and reader groups to evaluate the effectiveness of tagging augmented with control vocabulary for subject indexing of papers in online repositories. Findings - The results showed that using a knowledge organisation system to augment tagging does appear to increase the effectiveness of non-specialist users (that is, without information science training) in subject indexing. Research limitations/implications - While limited by the size and scope of the trials undertaken, these results do point to the usefulness of a mixed approach in supporting the subject indexing of online resources. Originality/value - The value of this work is as a guide to future developments in the practical support for resource indexing in online repositories.

Footnote

Beitrag in einem Special Issue: Content architecture: exploiting and managing diverse resources: proceedings of the first national conference of the United Kingdom chapter of the International Society for Knowedge Organization (ISKO)

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Abstract

This study will present a theoretical discussion about the semiotics categories and its application in the information organization. An experiment about the performance of the Gemet and Eurovoc thesauri with the subject "sustainable development" comparing with the folksonomies and distributed classification systems available on the online repositories of individual or collective information is presented. The new configuration of warrant (literary, structural and of usage) in the process of constructing indexing languages in digital environments will also be discussed. It suggested in the methodological terms that the new theoretical and informational mediations have to be incorporated in the construction process of indexing languages.

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Abstract

With the emergence of Web 2.0, sharing personal content, communicating ideas, and interacting with other online users in Web 2.0 communities have become daily routines for online users. User-generated data from Web 2.0 sites provide rich personal information (e.g., personal preferences and interests) and can be utilized to obtain insight about cyber communities and their social networks. Many studies have focused on leveraging user-generated information to analyze blogs and forums, but few studies have applied this approach to video-sharing Web sites. In this study, we propose a text-based framework for video content classification of online-video sharing Web sites. Different types of user-generated data (e.g., titles, descriptions, and comments) were used as proxies for online videos, and three types of text features (lexical, syntactic, and content-specific features) were extracted. Three feature-based classification techniques (C4.5, Naïve Bayes, and Support Vector Machine) were used to classify videos. To evaluate the proposed framework, user-generated data from candidate videos, which were identified by searching user-given keywords on YouTube, were first collected. Then, a subset of the collected data was randomly selected and manually tagged by users as our experiment data. The experimental results showed that the proposed approach was able to classify online videos based on users' interests with accuracy rates up to 87.2%, and all three types of text features contributed to discriminating videos. Support Vector Machine outperformed C4.5 and Naïve Bayes techniques in our experiments. In addition, our case study further demonstrated that accurate video-classification results are very useful for identifying implicit cyber communities on video-sharing Web sites.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.5, S.891-906

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Abstract

Purpose - This paper aims to discuss and test the claim that utilization of the personalization techniques can be valuable to improve the efficiency of collaborative information retrieval (CIR) systems. Design/methodology/approach - A new personalized CIR system, called PERCIRS, is presented based on the user profile similarity calculation (UPSC) formulas. To this aim, the paper proposes several UPSC formulas as well as two techniques to evaluate them. As the proposed CIR system is personalized, it could not be evaluated by Cranfield, like evaluation techniques (e.g. TREC). Hence, this paper proposes a new user-centric mechanism, which enables PERCIRS to be evaluated. This mechanism is generic and can be used to evaluate any other personalized IR system. Findings - The results show that among the proposed UPSC formulas in this paper, the (query-document)-graph based formula is the most effective. After integrating this formula into PERCIRS and comparing it with nine other IR systems, it is concluded that the results of the system are better than the other IR systems. In addition, the paper shows that the complexity of the system is less that the complexity of the other CIR systems. Research limitations/implications - This system asks the users to explicitly rank the returned documents, while explicit ranking is still not widespread enough. However it believes that the users should actively participate in the IR process in order to aptly satisfy their needs to information. Originality/value - The value of this paper lies in combining collaborative and personalized IR, as well as introducing a mechanism which enables the personalized IR system to be evaluated. The proposed evaluation mechanism is very valuable for developers of personalized IR systems. The paper also introduces some significant user profile similarity calculation formulas, and two techniques to evaluate them. These formulas can also be used to find the user's community in the social networks.

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Abstract

Because, to paraphrase a pop music lyric from a certain rock and roll band of yesterday, "the Web is old, the Web is new, the Web is all, the Web is you", it seems like we might have to face up to some of these stark realities. With the introduction of new social software applications such as blogs, wikis, newsfeeds, social networks, and bookmarking tools (the subject of this paper), the claim that Shelley Powers makes in a Burningbird blog entry seems apposite: "This is the user's web now, which means it's my web and I can make the rules." Reinvention is revolution - it brings us always back to beginnings. We are here going to remind you of hyperlinks in all their glory, sell you on the idea of bookmarking hyperlinks, point you at other folks who are doing the same, and tell you why this is a good thing. Just as long as those hyperlinks (or let's call them plain old links) are managed, tagged, commented upon, and published onto the Web, they represent a user's own personal library placed on public record, which - when aggregated with other personal libraries - allows for rich, social networking opportunities. Why spill any ink (digital or not) in rewriting what someone else has already written about instead of just pointing at the original story and adding the merest of titles, descriptions and tags for future reference? More importantly, why not make these personal 'link playlists' available to oneself and to others from whatever browser or computer one happens to be using at the time? This paper reviews some current initiatives, as of early 2005, in providing public link management applications on the Web - utilities that are often referred to under the general moniker of 'social bookmarking tools'. There are a couple of things going on here: 1) server-side software aimed specifically at managing links with, crucially, a strong, social networking flavour, and 2) an unabashedly open and unstructured approach to tagging, or user classification, of those links.
A number of such utilities are presented here, together with an emergent new class of tools that caters more to the academic communities and that stores not only user-supplied tags, but also structured citation metadata terms wherever it is possible to glean this information from service providers. This provision of rich, structured metadata means that the user is provided with an accurate third-party identification of a document, which could be used to retrieve that document, but is also free to search on user-supplied terms so that documents of interest (or rather, references to documents) can be made discoverable and aggregated with other similar descriptions either recorded by the user or by other users. Matt Biddulph in an XML.com article last year, in which he reviews one of the better known social bookmarking tools, del.icio.us, declares that the "del.icio.us-space has three major axes: users, tags, and URLs". We fully support that assessment but choose to present this deconstruction in a reverse order. This paper thus first recaps a brief history of bookmarks, then discusses the current interest in tagging, moves on to look at certain social issues, and finally considers some of the feature sets offered by the new bookmarking tools. A general review of a number of common social bookmarking tools is presented in the annex. A companion paper describes a case study in more detail: the tool that Nature Publishing Group has made available to the scientific community as an experimental entrée into this field - Connotea; our reasons for endeavouring to provide such a utility; and experiences gained and lessons learned.

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Abstract

Social bookmarking has gained popularity since the advent of Web 2.0. Keywords known as tags are created to annotate web content, and the resulting tag space composed of the tags, the resources, and the users arises as a new platform for web content discovery. Useful and interesting web resources can be located through searching and browsing based on tags, as well as following the user-user connections formed in the social bookmarking community. However, the effectiveness of tag-based search is limited due to the lack of explicitly represented semantics in the tag space. In addition, social connections between users are underused for web content discovery because of the inadequate social functions. In this research, we propose a comprehensive framework to reorganize the flat tag space into a hierarchical faceted model. We also studied the structure and properties of various networks emerging from the tag space for the purpose of more efficient web content discovery. The major research approach used in this research is social network analysis (SNA), together with methodologies employed in design science research. The contribution of our research includes: (i) a faceted model to categorize social bookmarking tags; (ii) a relationship ontology to represent the semantics of relationships between tags; (iii) heuristics to reorganize the flat tag space into a hierarchical faceted model using analysis of tag-tag co-occurrence networks; (iv) an implemented prototype system as proof-of-concept to validate the feasibility of the reorganization approach; (v) a set of evaluations of the social functions of the current networking features of social bookmarking and a series of recommendations as to how to improve the social functions to facilitate web content discovery.

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Abstract

Libraries are the tools we use to learn and to answer our questions. The quality of our work depends, among others, on the quality of the tools we use. Recent research in digital libraries is focused, on one hand on improving the infrastructure of the digital library management systems (DLMS), and on the other on improving the metadata models used to annotate collections of objects maintained by DLMS. The latter includes, among others, the semantic web and social networking technologies. Recently, the semantic web and social networking technologies are being introduced to the digital libraries domain. The expected outcome is that the overall quality of information discovery in digital libraries can be improved by employing social and semantic technologies. In this chapter we present the results of an evaluation of social and semantic end-user information discovery services for the digital libraries.

Date

1. 8.2010 12:35:22

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Abstract

The growing predominance of social semantics in the form of tagging presents the metadata community with both opportunities and challenges as for leveraging this new form of information content representation and for retrieval. One key challenge is the absence of contextual information associated with these tags. This paper presents an experiment working with Flickr tags as an example of utilizing social semantics sources for enriching subject metadata. The procedure included four steps: 1) Collecting a sample of Flickr tags, 2) Calculating cooccurrences between tags through mutual information, 3) Tracing contextual information of tag pairs via Google search results, 4) Applying natural language processing and machine learning techniques to extract semantic relations between tags. The experiment helped us to build a context sentence collection from the Google search results, which was then processed by natural language processing and machine learning algorithms. This new approach achieved a reasonably good rate of accuracy in assigning semantic relations to tag pairs. This paper also explores the implications of this approach for using social semantics to enrich subject metadata.

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

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Abstract

The purpose of this study was to examine user tags that describe digitized archival collections in the field of humanities. A collection of 8,310 tags from a digital portal (Nineteenth-Century Electronic Scholarship, NINES) was analyzed to find out what attributes of primary historical resources users described with tags. Tags were categorized to identify which tags describe the content of the resource, the resource itself, and subjective aspects (e.g., usage or emotion). The study's findings revealed that over half were content-related; tags representing opinion, usage context, or self-reference, however, reflected only a small percentage. The study further found that terms related to genre or physical format of a resource were frequently used in describing primary archival resources. It was also learned that nontextual resources had lower numbers of content-related tags and higher numbers of document-related tags than textual resources and bibliographic materials; moreover, textual resources tended to have more user-context-related tags than other resources. These findings help explain users' tagging behavior and resource interpretation in primary resources in the humanities. Such information provided through tags helps information professionals decide to what extent indexing archival and cultural resources should be done for resource description and discovery, and understand users' terminology.

Date

21. 4.2016 11:23:22

Source

Journal of the Association for Information Science and Technology. 67(2016) no.5, S.1089-1104

Theme

Information Gateway

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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

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Abstract

Describes three units of time helpful for understanding and evaluating classificatory structures: long time (versions and states of classification schemes), short time (the act of indexing as repeated ritual or form), and micro-time (where stages of the interpretation process of indexing are separated out and inventoried). Concludes with a short discussion of how time and the impermanence of classification also conjures up an artistic conceptualization of indexing, and briefly uses that to question the seemingly dominant understanding of classification practice as outcome of scientific management and assembly line thought.

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Abstract

A new collaborative approach in information organization and sharing has recently arisen, known as collaborative tagging or social indexing. A key element of collaborative tagging is the concept of collective intelligence (CI), which is a shared intelligence among all participants. This research investigates the phenomenon of social tagging in the context of CI with the aim to serve as a stepping-stone towards the mining of truly valuable social tags for web resources. This study focuses on assessing and evaluating the degree of CI embedded in social tagging over time in terms of two-parameter values, number of participants, and top frequency ranking window. Five different metrics were adopted and utilized for assessing the similarity between ranking lists: overlapList, overlapRank, Footrule, Fagin's measure, and the Inverse Rank measure. The result of this study demonstrates that a substantial degree of CI is most likely to be achieved when somewhere between the first 200 and 400 people have participated in tagging, and that a target degree of CI can be projected by controlling the two factors along with the selection of a similarity metric. The study also tests some experimental conditions for detecting social tags with high CI degree. The results of this study can be applicable to the study of filtering social tags based on CI; filtered social tags may be utilized for the metadata creation of tagged resources and possibly for the retrieval of tagged resources.

Date

25.12.2012 15:22:37

Source

Journal of the American Society for Information Science and Technology. 63(2012) no.12, S.2488-2502

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Abstract

Purpose Online knowledge communities make great contributions to global knowledge sharing and innovation. Resource tagging approaches have been widely adopted in such communities to describe, annotate and organize knowledge resources mainly through users' participation. However, it is unclear what causes the adoption of a particular resource tagging approach. The purpose of this paper is to identify factors that drive users to use a hybrid social tagging approach. Design/methodology/approach Technology acceptance model and social cognitive theory are adopted to support an integrated model proposed in this paper. Zhihu, one of the most popular online knowledge communities in China, is taken as the survey context. A survey was conducted with a questionnaire and collected data were analyzed through structural equation model. Findings A new hybrid social resource tagging approach was refined and described. The empirical results revealed that self-efficacy, perceived usefulness (PU) and perceived ease of use exert positive effect on users' attitude. Moreover, social influence, PU and attitude impact significantly on users' intention to use a hybrid social resource tagging approach. Originality/value Theoretically, this study enriches the type of resource tagging approaches and recognizes factors influencing user adoption to use it. Regarding the practical parts, the results provide online information system providers and designers with referential strategies to improve the performance of the current tagging approaches and promote them.

Date

20. 1.2015 18:30:22

Source

Aslib journal of information management. 71(2019) no.2, S.155-175

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Abstract

Social tagging empowers users to categorize content in a personally meaningful way while harnessing their potential to contribute to a collaborative construction of knowledge (Vander Wal, 2007). In addition, social tagging systems offer innovative filtering mechanisms that facilitate resource discovery and browsing (Mathes, 2004). As a result, social tags may support online communication, informal or intended learning as well as the development of online communities. The purpose of this mixed methods study is to examine how undergraduate students participate in social tagging activities in order to learn about their motivations, behaviours and practices. A better understanding of their knowledge, habits and interactions with such systems will help practitioners and developers identify important factors when designing enhancements. In the first phase of the study, students enrolled at a Canadian university completed 103 questionnaires. Quantitative results focusing on general familiarity with social tagging, frequently used Web 2.0 sites, and the purpose for engaging in social tagging activities were compiled. Eight questionnaire respondents participated in follow-up semi-structured interviews that further explored tagging practices by situating questionnaire responses within concrete experiences using popular websites such as YouTube, Facebook, Del.icio.us, and Flickr. Preliminary results of this study echo findings found in the growing literature concerning social tagging from the fields of computer science (Sen et al., 2006) and information science (Golder & Huberman, 2006; Macgregor & McCulloch, 2006). Generally, two classes of social taggers emerge: those who focus on tagging for individual purposes, and those who view tagging as a way to share or communicate meaning to others. Heavy del.icio.us users, for example, were often focused on simply organizing their own content, and seemed to be conscientiously maintaining their own personally relevant categorizations while, in many cases, placing little importance on the tags of others. Conversely, users tagging items primarily to share content preferred to use specific terms to optimize retrieval and discovery by others. Our findings should inform practitioners of how interaction design can be tailored for different tagging systems applications, and how these findings are positioned within the current debate surrounding social tagging among the resource discovery community. We also hope to direct future research in the field to place a greater importance on exploring the benefits of tagging as a socially-driven endeavour rather than uniquely as a means of managing information.

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

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Abstract

Many libraries and other cultural institutions are incorporating Web 2.0 features and enhanced metadata into their catalogs (Trant 2006). These value-added elements include those typically found in commercial and social networking sites, such as book jacket images, reviews, and usergenerated tags. One such site that libraries are exploring as a model is LibraryThing (www.librarything.com) LibraryThing is a social networking site that allows users to "catalog" their own book collections. Members can add tags and reviews to records for books, as well as engage in online discussions. In addition to its service for individuals, LibraryThing offers a feebased service to libraries, where institutions can add LibraryThing tags, recommendations, and other features to their online catalog records. This poster will present data analyzing the quality and quantity of the metadata that a large academic library would expect to gain if utilizing such a service, focusing on the overlap between titles found in the library's catalog and in LibraryThing's database, and on a comparison between the controlled subject headings in the former and the user-generated tags in the latter. During February through April 2008, a random sample of 383 titles from the University of Minnesota Libraries catalog was searched in LibraryThing. Eighty works, or 21 percent of the sample, had corresponding records available in LibraryThing. Golder and Huberman (2006) outline the advantages and disadvantages of using controlled vocabulary for subject access to information resources versus the growing trend of tags supplied by users or by content creators. Using the 80 matched records from the sample, comparisons were made between the user-supplied tags in LibraryThing (social tags) and the subject headings in the library catalog records (controlled vocabulary system). In the library records, terms from all 6XX MARC fields were used. To make a more meaningful comparison, controlled subject terms were broken down into facets according to their headings and subheadings, and each unique facet counted separately. A total of 227 subject terms were applied to the 80 catalog records, an average of 2.84 per record. In LibraryThing, 698 tags were applied to the same 80 titles, an average of 8.73 per title. The poster will further explore the relationships between the terms applied in each source, and identify where overlaps and complementary levels of access occur.

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

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Source

Annual review of information science and technology. 43(2009), S.xxx-xxx