Search (29 results, page 1 of 2)

  • × language_ss:"e"
  • × theme_ss:"Social tagging"
  • × type_ss:"a"
  1. Chen, M.; Liu, X.; Qin, J.: Semantic relation extraction from socially-generated tags : a methodology for metadata generation (2008) 0.03 
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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
  2. Yi, K.: Harnessing collective intelligence in social tagging using Delicious (2012) 0.03 
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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
  3. Bentley, C.M.; Labelle, P.R.: ¬A comparison of social tagging designs and user participation (2008) 0.02 
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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
  4. Konkova, E.; Göker, A.; Butterworth, R.; MacFarlane, A.: Social tagging: exploring the image, the tags, and the game (2014) 0.02 
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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.
  5. Catarino, M.E.; Baptista, A.A.: Relating folksonomies with Dublin Core (2008) 0.01 
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    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
  6. Golub, K.; Lykke, M.; Tudhope, D.: Enhancing social tagging with automated keywords from the Dewey Decimal Classification (2014) 0.01 
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    Abstract
    Purpose - The purpose of this paper is to explore the potential of applying the Dewey Decimal Classification (DDC) as an established knowledge organization system (KOS) for enhancing social tagging, with the ultimate purpose of improving subject indexing and information retrieval. Design/methodology/approach - Over 11.000 Intute metadata records in politics were used. Totally, 28 politics students were each given four tasks, in which a total of 60 resources were tagged in two different configurations, one with uncontrolled social tags only and another with uncontrolled social tags as well as suggestions from a controlled vocabulary. The controlled vocabulary was DDC comprising also mappings from the Library of Congress Subject Headings. Findings - The results demonstrate the importance of controlled vocabulary suggestions for indexing and retrieval: to help produce ideas of which tags to use, to make it easier to find focus for the tagging, to ensure consistency and to increase the number of access points in retrieval. The value and usefulness of the suggestions proved to be dependent on the quality of the suggestions, both as to conceptual relevance to the user and as to appropriateness of the terminology. Originality/value - No research has investigated the enhancement of social tagging with suggestions from the DDC, an established KOS, in a user trial, comparing social tagging only and social tagging enhanced with the suggestions. This paper is a final reflection on all aspects of the study.
  7. Vaidya, P.; Harinarayana, N.S.: ¬The comparative and analytical study of LibraryThing tags with Library of Congress Subject Headings (2016) 0.01 
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    Abstract
    The internet in its Web 2.0 version has given an opportunity among users to be participative and the chance to enhance the existing system, which makes it dynamic and collaborative. The activity of social tagging among researchers to organize the digital resources is an interesting study among information professionals. The one way of organizing the resources for future retrieval through these user-generated terms makes an interesting analysis by comparing them with professionally created controlled vocabularies. Here in this study, an attempt has been made to compare Library of Congress Subject Headings (LCSH) terms with LibraryThing social tags. In this comparative analysis, the results show that social tags can be used to enhance the metadata for information retrieval. But still, the uncontrolled nature of social tags is a concern and creates uncertainty among researchers.
  8. Kruk, S.R.; Kruk, E.; Stankiewicz, K.: Evaluation of semantic and social technologies for digital libraries (2009) 0.01 
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    Date
    1. 8.2010 12:35:22
  9. Rolla, P.J.: User tags versus Subject headings : can user-supplied data improve subject access to library collections? (2009) 0.01 
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    Date
    10. 9.2000 17:38:22
  10. Strader, C.R.: Author-assigned keywords versus Library of Congress Subject Headings : implications for the cataloging of electronic theses and dissertations (2009) 0.01 
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    Date
    10. 9.2000 17:38:22
  11. Sun, A.; Bhowmick, S.S.; Nguyen, K.T.N.; Bai, G.: Tag-based social image retrieval : an empirical evaluation (2011) 0.01 
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    Abstract
    Tags associated with social images are valuable information source for superior image search and retrieval experiences. Although various heuristics are valuable to boost tag-based search for images, there is a lack of general framework to study the impact of these heuristics. Specifically, the task of ranking images matching a given tag query based on their associated tags in descending order of relevance has not been well studied. In this article, we take the first step to propose a generic, flexible, and extensible framework for this task and exploit it for a systematic and comprehensive empirical evaluation of various methods for ranking images. To this end, we identified five orthogonal dimensions to quantify the matching score between a tagged image and a tag query. These five dimensions are: (i) tag relatedness to measure the degree of effectiveness of a tag describing the tagged image; (ii) tag discrimination to quantify the degree of discrimination of a tag with respect to the entire tagged image collection; (iii) tag length normalization analogous to document length normalization in web search; (iv) tag-query matching model for the matching score computation between an image tag and a query tag; and (v) query model for tag query rewriting. For each dimension, we identify a few implementations and evaluate their impact on NUS-WIDE dataset, the largest human-annotated dataset consisting of more than 269K tagged images from Flickr. We evaluated 81 single-tag queries and 443 multi-tag queries over 288 search methods and systematically compare their performances using standard metrics including Precision at top-K, Mean Average Precision (MAP), Recall, and Normalized Discounted Cumulative Gain (NDCG).
  12. Knautz, K.; Stock, W.G.: Collective indexing of emotions in videos (2011) 0.01 
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    Abstract
    Purpose - The object of this empirical research study is emotion, as depicted and aroused in videos. This paper seeks to answer the questions: Are users able to index such emotions consistently? Are the users' votes usable for emotional video retrieval? Design/methodology/approach - The authors worked with a controlled vocabulary for nine basic emotions (love, happiness, fun, surprise, desire, sadness, anger, disgust and fear), a slide control for adjusting the emotions' intensity, and the approach of broad folksonomies. Different users tagged the same videos. The test persons had the task of indexing the emotions of 20 videos (reprocessed clips from YouTube). The authors distinguished between emotions which were depicted in the video and those that were evoked in the user. Data were received from 776 participants and a total of 279,360 slide control values were analyzed. Findings - The consistency of the users' votes is very high; the tag distributions for the particular videos' emotions are stable. The final shape of the distributions will be reached by the tagging activities of only very few users (less than 100). By applying the approach of power tags it is possible to separate the pivotal emotions of every document - if indeed there is any feeling at all. Originality/value - This paper is one of the first steps in the new research area of emotional information retrieval (EmIR). To the authors' knowledge, it is the first research project into the collective indexing of emotions in videos.
  13. Ransom, N.; Rafferty, P.: Facets of user-assigned tags and their effectiveness in image retrieval (2011) 0.01 
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    Abstract
    Purpose - This study aims to consider the value of user-assigned image tags by comparing the facets that are represented in image tags with those that are present in image queries to see if there is a similarity in the way that users describe and search for images. Design/methodology/approach - A sample dataset was created by downloading a selection of images and associated tags from Flickr, the online photo-sharing web site. The tags were categorised using image facets from Shatford's matrix, which has been widely used in previous research into image indexing and retrieval. The facets present in the image tags were then compared with the results of previous research into image queries. Findings - The results reveal that there are broad similarities between the facets present in image tags and queries, with people and objects being the most common facet, followed by location. However, the results also show that there are differences in the level of specificity between tags and queries, with image tags containing more generic terms and image queries consisting of more specific terms. The study concludes that users do describe and search for images using similar image facets, but that measures to close the gap between specific queries and generic tags would improve the value of user tags in indexing image collections. Originality/value - Research into tagging has tended to focus on textual resources with less research into non-textual documents. In particular, little research has been undertaken into how user tags compare to the terms used in search queries, particularly in the context of digital images.
  14. Kim, H.L.; Scerri, S.; Breslin, J.G.; Decker, S.; Kim, H.G.: ¬The state of the art in tag ontologies : a semantic model for tagging and folksonomies (2008) 0.01 
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    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
  15. Choi, Y.; Syn, S.Y.: Characteristics of tagging behavior in digitized humanities online collections (2016) 0.01 
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    21. 4.2016 11:23:22
  16. Qin, C.; Liu, Y.; Mou, J.; Chen, J.: User adoption of a hybrid social tagging approach in an online knowledge community (2019) 0.01 
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    Date
    20. 1.2015 18:30:22
  17. Rafferty, P.; Hidderley, R.: Flickr and democratic Indexing : dialogic approaches to indexing (2007) 0.01 
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    Abstract
    Purpose - The purpose of this paper is two-fold: to examine three models of subject indexing (i.e. expert-led indexing, author-generated indexing, and user-orientated indexing); and to compare and contrast two user-orientated indexing approaches (i.e. the theoretically-based Democratic Indexing project, and Flickr, a working system for describing photographs). Design/methodology/approach - The approach to examining Flickr and Democratic Indexing is evaluative. The limitations of Flickr are described and examples are provided. The Democratic Indexing approach, which the authors believe offers a method of marshalling a "free" user-indexed archive to provide useful retrieval functions, is described. Findings - The examination of both Flickr and the Democratic Indexing approach suggests that, despite Shirky's claim of philosophical paradigm shifting for social tagging, there is a residing doubt amongst information professionals that self-organising systems can work without there being some element of control and some form of "representative authority". Originality/value - This paper contributes to the literature of user-based indexing and social tagging.
  18. Kipp, M.E.I.: Searching with tags : do tags help users find things? (2008) 0.01 
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    Content
    This study examines the question of whether tags can be useful in the process of information retrieval. Participants were asked to search a social bookmarking tool specialising in academic articles (CiteULike) and an online journal database (Pubmed) in order to determine if users found tags were useful in their search process. The actions of each participants were captured using screen capture software and they were asked to describe their search process. The preliminary study showed that users did indeed make use of tags in their search process, as a guide to searching and as hyperlinks to potentially useful articles. However, users also made use of controlled vocabularies in the journal database.
  19. Wang, J.; Clements, M.; Yang, J.; Vries, A.P. de; Reinders, M.J.T.: Personalization of tagging systems (2010) 0.01 
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    Abstract
    Social media systems have encouraged end user participation in the Internet, for the purpose of storing and distributing Internet content, sharing opinions and maintaining relationships. Collaborative tagging allows users to annotate the resulting user-generated content, and enables effective retrieval of otherwise uncategorised data. However, compared to professional web content production, collaborative tagging systems face the challenge that end-users assign tags in an uncontrolled manner, resulting in unsystematic and inconsistent metadata. This paper introduces a framework for the personalization of social media systems. We pinpoint three tasks that would benefit from personalization: collaborative tagging, collaborative browsing and collaborative search. We propose a ranking model for each task that integrates the individual user's tagging history in the recommendation of tags and content, to align its suggestions to the individual user preferences. We demonstrate on two real data sets that for all three tasks, the personalized ranking should take into account both the user's own preference and the opinion of others.
  20. Naderi, H.; Rumpler, B.: PERCIRS: a system to combine personalized and collaborative information retrieval (2010) 0.01 
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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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